gaojie
818 lines
22 KiB
Markdown
818 lines
22 KiB
Markdown
# ZED 2i 双目+IMU 完整解决方案
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## 📋 方案概述
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本方案基于Stereolabs ZED 2i双目相机，集成IMU传感器，提供实时3D感知和位姿估计能力，是性价比最高的一体化视觉惯性方案。
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### 核心优势
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- 💰 **成本低**：¥3,999（单设备完整方案）
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- ⚡ **实时性**：15-30 FPS深度计算
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- 🎯 **精度高**：深度精度±1-2%，位姿精度±0.1%
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- 🔧 **易集成**：SDK完善，支持多平台
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- 🤖 **即插即用**：无需标定，开箱可用
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### 适用场景
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- ✅ 室内移动机器人导航
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- ✅ 实时3D建图与定位
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- ✅ AR/VR应用
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- ✅ 无人机室内飞行
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- ✅ 酒店场景快速扫描
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---
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## 一、硬件规格详解
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### 1.1 ZED 2i 技术参数
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```yaml
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基本信息:
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型号: ZED 2i
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制造商: Stereolabs
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价格: ¥3,999
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重量: 159g
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尺寸: 175mm × 30mm × 33mm
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双目相机:
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传感器: 2× 2.1MP CMOS
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分辨率:
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- 2.2K: 2208×1242 @ 15fps
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- 1080p: 1920×1080 @ 30fps
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- 720p: 1280×720 @ 60fps
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- WVGA: 1344×376 @ 100fps
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基线距离: 120mm
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视场角: 110° (H) × 70° (V)
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快门: 全局快门（避免运动模糊）
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深度感知:
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测距范围: 0.3m - 20m
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深度精度: ±1-2% @ 1-3m
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深度模式:
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- PERFORMANCE: 高速
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- QUALITY: 高质量
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- ULTRA: 超高质量
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最小深度: 0.3m (近距离模式)
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IMU传感器:
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类型: 6轴IMU (加速度计 + 陀螺仪)
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采样率: 400 Hz
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加速度计范围: ±16g
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陀螺仪范围: ±2000 dps
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温度补偿: 是
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其他特性:
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接口: USB 3.0 Type-C
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功耗: 3.5W (典型)
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工作温度: 0°C - 45°C
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防护等级: IP44 (防尘防溅)
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```
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### 1.2 与其他双目相机对比
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| 型号 | 价格 | 基线 | 分辨率 | IMU | 深度精度 | 推荐度 |
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|-----|------|------|--------|-----|---------|--------|
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| **ZED 2i** | ¥3,999 | 120mm | 2.2K@15fps | ✅ 400Hz | ±1-2% | ⭐⭐⭐⭐⭐ |
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| ZED 2 | ¥2,999 | 120mm | 2.2K@15fps | ✅ 100Hz | ±1-2% | ⭐⭐⭐⭐ |
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| ZED X | ¥5,999 | 120mm | 1.2MP@60fps | ✅ 400Hz | ±1% | ⭐⭐⭐⭐ |
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| RealSense D455 | ¥2,999 | 95mm | 1280×720 | ❌ | ±2% | ⭐⭐⭐ |
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| OAK-D Pro | ¥4,999 | 75mm | 4K@30fps | ✅ | ±1% | ⭐⭐⭐⭐ |
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**结论**：ZED 2i在价格、性能、IMU质量的平衡上最优。
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---
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## 二、软件生态与SDK
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### 2.1 ZED SDK
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```yaml
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支持平台:
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- Windows 10/11
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- Ubuntu 18.04/20.04/22.04
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- Jetson (Nano, Xavier, Orin)
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- Docker
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编程语言:
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- C++
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- Python
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- C#
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- Unity
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- Unreal Engine
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核心模块:
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- Camera: 相机控制
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- Depth: 深度计算
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- Tracking: 位姿估计
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- Spatial Mapping: 3D建图
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- Object Detection: 物体检测
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- Body Tracking: 人体追踪
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ROS支持:
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- ROS 1 (Melodic, Noetic)
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- ROS 2 (Foxy, Humble)
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```
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### 2.2 安装配置
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```bash
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# Ubuntu安装ZED SDK
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# 1. 下载SDK
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wget https://download.stereolabs.com/zedsdk/4.0/cu118/ubuntu22
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# 2. 安装
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chmod +x ZED_SDK_Ubuntu22_cuda11.8_v4.0.run
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./ZED_SDK_Ubuntu22_cuda11.8_v4.0.run
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# 3. 安装Python包
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pip install pyzed
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# 4. 验证安装
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ZED_Explorer
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# ROS 2安装
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sudo apt install ros-humble-zed-ros2
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```
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---
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## 三、核心功能实现
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### 3.1 基础采集
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```python
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# basic_capture.py
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import pyzed.sl as sl
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import numpy as np
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import cv2
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class ZED2iCamera:
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"""ZED 2i相机封装"""
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def __init__(self):
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# 创建相机对象
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self.zed = sl.Camera()
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# 配置参数
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self.init_params = sl.InitParameters()
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self.init_params.camera_resolution = sl.RESOLUTION.HD1080
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self.init_params.camera_fps = 30
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self.init_params.depth_mode = sl.DEPTH_MODE.ULTRA
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self.init_params.coordinate_units = sl.UNIT.METER
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self.init_params.depth_minimum_distance = 0.3
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self.init_params.depth_maximum_distance = 20.0
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# 打开相机
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err = self.zed.open(self.init_params)
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if err != sl.ERROR_CODE.SUCCESS:
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print(f"Error opening camera: {err}")
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exit(1)
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# 创建图像容器
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self.image_left = sl.Mat()
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self.image_right = sl.Mat()
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self.depth_map = sl.Mat()
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self.point_cloud = sl.Mat()
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# 运行时参数
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self.runtime_params = sl.RuntimeParameters()
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self.runtime_params.sensing_mode = sl.SENSING_MODE.STANDARD
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print("ZED 2i initialized successfully")
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def grab_frame(self):
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"""采集一帧数据"""
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if self.zed.grab(self.runtime_params) == sl.ERROR_CODE.SUCCESS:
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# 获取左右图像
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self.zed.retrieve_image(self.image_left, sl.VIEW.LEFT)
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self.zed.retrieve_image(self.image_right, sl.VIEW.RIGHT)
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# 获取深度图
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self.zed.retrieve_measure(self.depth_map, sl.MEASURE.DEPTH)
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# 获取点云
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self.zed.retrieve_measure(self.point_cloud, sl.MEASURE.XYZRGBA)
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return True
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return False
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def get_images(self):
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"""获取RGB图像"""
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left_img = self.image_left.get_data()
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right_img = self.image_right.get_data()
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return left_img, right_img
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def get_depth(self):
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"""获取深度图"""
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depth = self.depth_map.get_data()
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return depth
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def get_pointcloud(self):
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"""获取点云"""
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pc = self.point_cloud.get_data()
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return pc
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def close(self):
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"""关闭相机"""
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self.zed.close()
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# 使用示例
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camera = ZED2iCamera()
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while True:
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if camera.grab_frame():
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# 获取数据
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left, right = camera.get_images()
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depth = camera.get_depth()
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# 显示
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cv2.imshow("Left", left)
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cv2.imshow("Depth", depth / 20.0) # 归一化到0-1
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if cv2.waitKey(1) & 0xFF == ord('q'):
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break
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camera.close()
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cv2.destroyAllWindows()
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```
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### 3.2 位姿追踪（Tracking）
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```python
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# tracking.py
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import pyzed.sl as sl
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import numpy as np
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class ZED2iTracking:
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"""ZED 2i位姿追踪"""
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def __init__(self):
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self.zed = sl.Camera()
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# 初始化参数
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init_params = sl.InitParameters()
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init_params.camera_resolution = sl.RESOLUTION.HD720
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init_params.camera_fps = 60
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init_params.coordinate_units = sl.UNIT.METER
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init_params.coordinate_system = sl.COORDINATE_SYSTEM.RIGHT_HANDED_Z_UP
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# 打开相机
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err = self.zed.open(init_params)
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if err != sl.ERROR_CODE.SUCCESS:
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exit(1)
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# 启用位姿追踪
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tracking_params = sl.PositionalTrackingParameters()
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tracking_params.enable_imu_fusion = True # 启用IMU融合
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tracking_params.enable_area_memory = True # 启用区域记忆
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err = self.zed.enable_positional_tracking(tracking_params)
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if err != sl.ERROR_CODE.SUCCESS:
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print(f"Error enabling tracking: {err}")
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exit(1)
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# 位姿对象
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self.camera_pose = sl.Pose()
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self.imu_data = sl.SensorsData()
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print("Tracking initialized")
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def get_pose(self):
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"""获取相机位姿"""
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if self.zed.grab() == sl.ERROR_CODE.SUCCESS:
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# 获取位姿
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tracking_state = self.zed.get_position(
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self.camera_pose,
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sl.REFERENCE_FRAME.WORLD
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)
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if tracking_state == sl.POSITIONAL_TRACKING_STATE.OK:
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# 提取位置和旋转
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translation = self.camera_pose.get_translation().get()
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rotation = self.camera_pose.get_rotation_matrix().r
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# 获取IMU数据
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self.zed.get_sensors_data(
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self.imu_data,
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sl.TIME_REFERENCE.IMAGE
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)
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imu = self.imu_data.get_imu_data()
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return {
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'position': translation,
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'rotation': rotation,
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'tracking_state': tracking_state,
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'imu': {
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'linear_acceleration': imu.get_linear_acceleration(),
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'angular_velocity': imu.get_angular_velocity(),
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'orientation': imu.get_pose().get_orientation().get()
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}
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}
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return None
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def reset_tracking(self):
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"""重置追踪"""
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self.zed.reset_positional_tracking(sl.Transform())
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def save_area_memory(self, filename):
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"""保存区域记忆"""
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self.zed.save_area_memory(filename)
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def load_area_memory(self, filename):
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"""加载区域记忆"""
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self.zed.load_area_memory(filename)
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# 使用示例
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tracker = ZED2iTracking()
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trajectory = []
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while True:
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pose_data = tracker.get_pose()
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if pose_data:
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pos = pose_data['position']
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trajectory.append(pos)
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print(f"Position: x={pos[0]:.2f}, y={pos[1]:.2f}, z={pos[2]:.2f}")
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print(f"IMU Accel: {pose_data['imu']['linear_acceleration']}")
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# 每100帧保存一次轨迹
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if len(trajectory) % 100 == 0:
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np.save('trajectory.npy', np.array(trajectory))
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# 保存区域记忆
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tracker.save_area_memory("hotel_room.area")
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```
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### 3.3 空间建图（Spatial Mapping）
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```python
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# spatial_mapping.py
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import pyzed.sl as sl
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import numpy as np
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class ZED2iMapping:
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"""ZED 2i 3D建图"""
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def __init__(self):
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self.zed = sl.Camera()
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# 初始化
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init_params = sl.InitParameters()
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init_params.camera_resolution = sl.RESOLUTION.HD720
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init_params.camera_fps = 30
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init_params.depth_mode = sl.DEPTH_MODE.ULTRA
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init_params.coordinate_units = sl.UNIT.METER
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self.zed.open(init_params)
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# 启用追踪
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tracking_params = sl.PositionalTrackingParameters()
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tracking_params.enable_imu_fusion = True
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self.zed.enable_positional_tracking(tracking_params)
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# 启用空间建图
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mapping_params = sl.SpatialMappingParameters()
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mapping_params.resolution_meter = 0.05 # 5cm分辨率
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mapping_params.range_meter = 10.0 # 10m范围
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mapping_params.use_chunk_only = False
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mapping_params.save_texture = True
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mapping_params.map_type = sl.SPATIAL_MAP_TYPE.FUSED_POINT_CLOUD
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self.zed.enable_spatial_mapping(mapping_params)
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self.mesh = sl.Mesh()
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self.fused_pc = sl.FusedPointCloud()
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print("Spatial mapping initialized")
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def update_map(self):
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"""更新地图"""
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if self.zed.grab() == sl.ERROR_CODE.SUCCESS:
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# 获取建图状态
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mapping_state = self.zed.get_spatial_mapping_state()
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return mapping_state
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return None
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def extract_mesh(self, output_path="room.obj"):
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"""提取网格"""
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# 停止建图
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self.zed.pause_spatial_mapping(True)
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# 提取网格
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self.zed.extract_whole_spatial_map(self.mesh)
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# 过滤网格
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self.mesh.filter(
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sl.MeshFilterParameters.MESH_FILTER.LOW,
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update_chunk_only=False
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)
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# 保存
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self.mesh.save(output_path)
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print(f"Mesh saved to {output_path}")
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print(f"Vertices: {self.mesh.vertices.shape[0]}")
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print(f"Triangles: {self.mesh.triangles.shape[0]}")
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# 恢复建图
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self.zed.pause_spatial_mapping(False)
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def extract_pointcloud(self, output_path="room.ply"):
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"""提取点云"""
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self.zed.pause_spatial_mapping(True)
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# 提取融合点云
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self.zed.extract_whole_spatial_map(self.fused_pc)
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# 保存
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self.fused_pc.save(output_path)
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print(f"Point cloud saved to {output_path}")
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print(f"Points: {self.fused_pc.vertices.shape[0]}")
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self.zed.pause_spatial_mapping(False)
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def get_map_statistics(self):
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"""获取地图统计信息"""
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stats = {
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'chunks': self.zed.get_spatial_mapping_state().number_of_chunks,
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'vertices': self.mesh.vertices.shape[0] if self.mesh.vertices.size > 0 else 0,
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'triangles': self.mesh.triangles.shape[0] if self.mesh.triangles.size > 0 else 0
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}
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return stats
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# 使用示例
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mapper = ZED2iMapping()
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print("开始建图，请移动相机扫描房间...")
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print("按 's' 保存地图，按 'q' 退出")
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frame_count = 0
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while True:
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state = mapper.update_map()
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if state:
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frame_count += 1
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if frame_count % 30 == 0: # 每秒显示一次
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stats = mapper.get_map_statistics()
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print(f"Chunks: {stats['chunks']}, "
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f"Vertices: {stats['vertices']}, "
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f"Triangles: {stats['triangles']}")
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# 键盘控制
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key = cv2.waitKey(1) & 0xFF
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if key == ord('s'):
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print("保存地图...")
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mapper.extract_mesh("hotel_room.obj")
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mapper.extract_pointcloud("hotel_room.ply")
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elif key == ord('q'):
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break
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print("建图完成")
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```
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### 3.4 物体检测（Object Detection）
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```python
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# object_detection.py
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import pyzed.sl as sl
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class ZED2iObjectDetection:
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"""ZED 2i物体检测"""
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def __init__(self):
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self.zed = sl.Camera()
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# 初始化
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init_params = sl.InitParameters()
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init_params.camera_resolution = sl.RESOLUTION.HD720
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init_params.camera_fps = 30
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init_params.depth_mode = sl.DEPTH_MODE.ULTRA
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self.zed.open(init_params)
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# 启用追踪
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tracking_params = sl.PositionalTrackingParameters()
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self.zed.enable_positional_tracking(tracking_params)
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# 启用物体检测
|
||
obj_det_params = sl.ObjectDetectionParameters()
|
||
obj_det_params.enable_tracking = True
|
||
obj_det_params.enable_segmentation = False
|
||
obj_det_params.detection_model = sl.OBJECT_DETECTION_MODEL.MULTI_CLASS_BOX_FAST
|
||
|
||
self.zed.enable_object_detection(obj_det_params)
|
||
|
||
self.objects = sl.Objects()
|
||
self.obj_runtime_params = sl.ObjectDetectionRuntimeParameters()
|
||
self.obj_runtime_params.detection_confidence_threshold = 50
|
||
|
||
print("Object detection initialized")
|
||
|
||
def detect_objects(self):
|
||
"""检测物体"""
|
||
if self.zed.grab() == sl.ERROR_CODE.SUCCESS:
|
||
# 检测物体
|
||
self.zed.retrieve_objects(
|
||
self.objects,
|
||
self.obj_runtime_params
|
||
)
|
||
|
||
detected = []
|
||
|
||
for obj in self.objects.object_list:
|
||
detected.append({
|
||
'id': obj.id,
|
||
'label': obj.label,
|
||
'confidence': obj.confidence,
|
||
'position': obj.position,
|
||
'velocity': obj.velocity,
|
||
'bounding_box_2d': obj.bounding_box_2d,
|
||
'bounding_box_3d': obj.bounding_box
|
||
})
|
||
|
||
return detected
|
||
|
||
return []
|
||
|
||
# 使用示例
|
||
detector = ZED2iObjectDetection()
|
||
|
||
while True:
|
||
objects = detector.detect_objects()
|
||
|
||
for obj in objects:
|
||
print(f"Object {obj['id']}: {obj['label']} "
|
||
f"({obj['confidence']:.0f}%) "
|
||
f"at {obj['position']}")
|
||
```
|
||
|
||
---
|
||
|
||
## 四、酒店场景应用方案
|
||
|
||
### 4.1 客房快速扫描
|
||
|
||
```yaml
|
||
应用场景: 5分钟完成客房3D建模
|
||
|
||
硬件配置:
|
||
- ZED 2i相机: ¥3,999
|
||
- 手持稳定器: ¥899
|
||
- 笔记本电脑: ¥8,000
|
||
总成本: ¥12,898
|
||
|
||
扫描流程:
|
||
1. 启动ZED 2i + 空间建图
|
||
2. 从门口开始环绕房间
|
||
3. 保持1-1.5m距离
|
||
4. 移动速度: 0.3-0.5 m/s
|
||
5. 重点扫描家具和角落
|
||
6. 实时查看建图进度
|
||
7. 完成后导出Mesh
|
||
|
||
输出结果:
|
||
- 3D网格模型（OBJ）
|
||
- 融合点云（PLY）
|
||
- 相机轨迹（TXT）
|
||
- 区域记忆（AREA）
|
||
|
||
精度指标:
|
||
- 几何精度: ±3-5cm
|
||
- 覆盖率: > 90%
|
||
- 采集时间: 5-10分钟
|
||
- 处理时间: 实时
|
||
```
|
||
|
||
### 4.2 机器人导航
|
||
|
||
```yaml
|
||
应用场景: 服务机器人室内导航
|
||
|
||
系统架构:
|
||
ZED 2i → ROS 2 → Navigation Stack → 机器人控制
|
||
|
||
ROS 2节点:
|
||
- zed_wrapper: 相机驱动
|
||
- zed_tracking: 位姿发布
|
||
- zed_mapping: 地图构建
|
||
- nav2: 导航规划
|
||
|
||
功能实现:
|
||
- 实时SLAM建图
|
||
- 自主定位
|
||
- 路径规划
|
||
- 障碍物避让
|
||
- 动态重规划
|
||
|
||
性能指标:
|
||
- 定位精度: ±5cm
|
||
- 更新频率: 30 Hz
|
||
- 地图分辨率: 5cm
|
||
- 最大速度: 1 m/s
|
||
```
|
||
|
||
### 4.3 AR导览系统
|
||
|
||
```yaml
|
||
应用场景: 酒店AR导览与信息叠加
|
||
|
||
技术栈:
|
||
- ZED 2i: 位姿追踪
|
||
- Unity: AR渲染
|
||
- ZED Unity Plugin: 集成
|
||
|
||
功能特性:
|
||
- 实时位姿追踪
|
||
- 虚拟信息叠加
|
||
- 空间锚点
|
||
- 遮挡处理
|
||
|
||
应用示例:
|
||
- 房间导航箭头
|
||
- 设施信息标注
|
||
- 虚拟导游
|
||
- 互动游戏
|
||
```
|
||
|
||
---
|
||
|
||
## 五、性能优化
|
||
|
||
### 5.1 参数调优
|
||
|
||
```python
|
||
# 不同场景的最佳参数
|
||
|
||
# 场景1：高速移动（机器人）
|
||
init_params.camera_resolution = sl.RESOLUTION.HD720
|
||
init_params.camera_fps = 60
|
||
init_params.depth_mode = sl.DEPTH_MODE.PERFORMANCE
|
||
|
||
# 场景2：高质量建图（静态扫描）
|
||
init_params.camera_resolution = sl.RESOLUTION.HD1080
|
||
init_params.camera_fps = 15
|
||
init_params.depth_mode = sl.DEPTH_MODE.ULTRA
|
||
|
||
# 场景3：实时AR（低延迟）
|
||
init_params.camera_resolution = sl.RESOLUTION.HD720
|
||
init_params.camera_fps = 60
|
||
init_params.depth_mode = sl.DEPTH_MODE.PERFORMANCE
|
||
runtime_params.enable_depth = False # 仅追踪
|
||
|
||
# 场景4：远距离检测
|
||
init_params.depth_minimum_distance = 1.0
|
||
init_params.depth_maximum_distance = 20.0
|
||
init_params.depth_mode = sl.DEPTH_MODE.ULTRA
|
||
```
|
||
|
||
### 5.2 性能基准
|
||
|
||
| 配置 | 分辨率 | FPS | 深度模式 | CPU占用 | GPU占用 | 延迟 |
|
||
|-----|--------|-----|---------|---------|---------|------|
|
||
| 高速 | 720p | 60 | PERFORMANCE | 15% | 20% | 16ms |
|
||
| 标准 | 1080p | 30 | QUALITY | 25% | 35% | 33ms |
|
||
| 高质量 | 1080p | 15 | ULTRA | 35% | 50% | 66ms |
|
||
| 超高质量 | 2.2K | 15 | ULTRA | 45% | 65% | 66ms |
|
||
|
||
**测试平台**：Intel i7-11800H + RTX 3060 Laptop
|
||
|
||
---
|
||
|
||
## 六、故障排查
|
||
|
||
### 6.1 常见问题
|
||
|
||
```yaml
|
||
问题1: 相机无法打开
|
||
原因: USB带宽不足
|
||
解决: 使用USB 3.0接口，避免USB Hub
|
||
|
||
问题2: 追踪丢失
|
||
原因: 纹理不足或运动过快
|
||
解决: 降低移动速度，增加环境纹理
|
||
|
||
问题3: 深度图有空洞
|
||
原因: 反光表面或透明物体
|
||
解决: 调整光照，使用ULTRA模式
|
||
|
||
问题4: IMU数据不稳定
|
||
原因: 温度漂移或磁干扰
|
||
解决: 预热5分钟，远离磁场
|
||
|
||
问题5: 建图不完整
|
||
原因: 移动过快或覆盖不足
|
||
解决: 降低速度，增加重叠率
|
||
```
|
||
|
||
### 6.2 性能优化建议
|
||
|
||
```python
|
||
# 优化技巧
|
||
|
||
# 1. 降低分辨率提升帧率
|
||
init_params.camera_resolution = sl.RESOLUTION.HD720 # 而非HD1080
|
||
|
||
# 2. 使用GPU加速
|
||
init_params.sdk_gpu_id = 0 # 指定GPU
|
||
|
||
# 3. 禁用不需要的功能
|
||
runtime_params.enable_depth = False # 仅需RGB时
|
||
|
||
# 4. 批量处理
|
||
# 每N帧处理一次深度，而非每帧
|
||
|
||
# 5. 异步处理
|
||
# 使用多线程分离采集和处理
|
||
```
|
||
|
||
---
|
||
|
||
## 七、完整项目示例
|
||
|
||
### 7.1 酒店客房扫描系统
|
||
|
||
```python
|
||
# hotel_room_scanner.py
|
||
import pyzed.sl as sl
|
||
import cv2
|
||
import numpy as np
|
||
import time
|
||
|
||
class HotelRoomScanner:
|
||
"""酒店客房扫描系统"""
|
||
|
||
def __init__(self, room_id):
|
||
self.room_id = room_id
|
||
self.zed = sl.Camera()
|
||
|
||
# 初始化相机
|
||
init_params = sl.InitParameters()
|
||
init_params.camera_resolution = sl.RESOLUTION.HD1080
|
||
init_params.camera_fps = 30
|
||
init_params.depth_mode = sl.DEPTH_MODE.ULTRA
|
||
init_params.coordinate_units = sl.UNIT.METER
|
||
|
||
err = self.zed.open(init_params)
|
||
if err != sl.ERROR_CODE.SUCCESS:
|
||
print(f"Error: {err}")
|
||
exit(1)
|
||
|
||
# 启用追踪
|
||
tracking_params = sl.PositionalTrackingParameters()
|
||
tracking_params.enable_imu_fusion = True
|
||
tracking_params.enable_area_memory = True
|
||
self.zed.enable_positional_tracking(tracking_params)
|
||
|
||
# 启用建图
|
||
mapping_params = sl.SpatialMappingParameters()
|
||
mapping_params.resolution_meter = 0.05
|
||
mapping_params.range_meter = 10.0
|
||
mapping_params.save_texture = True
|
||
self.zed.enable_spatial_mapping(mapping_params)
|
||
|
||
# 数据容器
|
||
self.image = sl.Mat()
|
||
self.depth = sl.Mat()
|
||
self.mesh = sl.Mesh()
|
||
self.pose = sl.Pose()
|
||
|
||
# 统计
|
||
self.start_time = time.time()
|
||
self.frame_count = 0
|
||
|
||
print(f"Room {room_id} scanner initialized")
|
||
|
||
def scan(self, duration_seconds=300):
|
||
"""扫描房间"""
|
||
print(f"开始扫描房间 {self.room_id}")
|
||
print(f"扫描时长: {duration_seconds}秒")
|
||
print("请缓慢移动相机环绕房间...")
|
||
|
||
while (time.time() - self.start_time) < duration_seconds:
|
||
if self.zed.grab() == sl.ERROR_CODE.SUCCESS:
|
||
self.frame_count += 1
|
||
|
||
# 获取图像和深度
|
||
self.zed.retrieve_image(self.image, sl.VIEW.LEFT)
|
||
self.zed.retrieve_measure(self.depth, sl.MEASURE.DEPTH)
|
||
|
||
# 获取位姿
|
||
state = self.zed.get_position(self.pose, sl.REFERENCE_FRAME.WORLD)
|
||
|
||
# 显示进度
|
||
if self.frame_count % 30 == 0:
|
||
elapsed = time.time() - self.start_time
|
||
remaining = duration_seconds - elapsed
|
||
|
||
stats = self.zed.get_spatial_mapping_state()
|
||
|
||
print(f"[{elapsed:.0f}s/{duration_seconds}s] " |