gaojie
377 lines
14 KiB
Markdown
377 lines
14 KiB
Markdown
---
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title: "Chapter 05 — 管线 B：ZED 2i 重定位握手"
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date: 2026-05-20
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draft: false
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tags: ["PRISM", "世界模型", "空间记忆", "VIO", "点云", "机器人"]
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categories: ["PRISM"]
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---
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# Chapter 05 — 管线 B：ZED 2i 重定位握手
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> 本章目标：机器人上电后，**用 ZED 2i 当前观测把自己"安放"到 iPhone 先验地图的 `map` 坐标系里**——这是两个方案能合体的"握手时刻"。
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---
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## 5.1 为什么必须重定位
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ZED 2i 自己能跑 VIO，但 VIO 给出的位姿是相对于"**ZED 开机时所在的某点**"，与 iPhone `map` 帧无关。如果不做重定位：
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- ❌ 机器人查 LTM 时，「床」的全局坐标对它毫无意义
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- ❌ 多次开机后，每次的"原点"都不同
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- ❌ 长距离漂移无法用先验校正
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**重定位 = 计算一个一次性的 `T_zed→map`，把后续所有 ZED VIO 输出都左乘这个矩阵。**
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---
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## 5.2 触发条件
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| 触发场景 | 模式 |
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|----------|------|
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| 上电首次 | **冷启动**：无任何先验位姿 |
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| 长时间漂移（> 30 s 未匹配 anchor） | **温启动**：有粗略先验 |
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| 跟丢 / 绑架（被人抱起放下） | **绑架恢复**：可能瞬移到任意房间 |
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| 跨房间穿门 | **过门校正**（轻量）：仅刷新位姿不重头 |
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| 周期性（每 5 min） | **健康检查**：核对漂移 |
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冷启动与绑架恢复需要全局检索，温启动只在小范围验证。
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---
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## 5.3 两段式重定位策略：粗 → 精
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```mermaid
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flowchart TB
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S1["<b>Stage 1: 粗匹配</b>（CLIP / DINO 视觉指纹）<br/>──────<br/>输入：当前 ZED RGB 帧<br/>输出：Top-K 候选房间 (L3 nodes)<br/>耗时：50–150 ms<br/>召回率目标：Top-3 > 95%"]
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S2["<b>Stage 2: 精配准</b>（点云 ICP / TEASER++）<br/>──────<br/>输入：ZED 当前点云 vs 候选房间 mesh/anchor<br/>输出：T_zed→map 及其 fitness 分数<br/>耗时：200–800 ms<br/>位置误差目标：< 10 cm"]
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Q{"fitness > 0.7 ?"}
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OK(["接受，发布 TF"])
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FB(["退到下一候选 / 走 Stage 1.b 兜底"])
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S1 --> S2 --> Q
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Q -- yes --> OK
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Q -- no --> FB
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style S1 fill:#e3f2fd,stroke:#1565c0
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style S2 fill:#fff7d6,stroke:#c97a00
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style OK fill:#d4f0d4,stroke:#2e7d32
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style FB fill:#fde2e2,stroke:#a33
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```
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---
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## 5.4 Stage 1 — 视觉粗匹配
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### 5.4.1 CLIP 房间检索（默认方案）
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```python
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# spatial_memory/relocalize_coarse.py
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import open_clip, torch, numpy as np
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class CoarseRelocalizer:
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def __init__(self, mem: SpatialMemory):
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self.mem = mem
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self.model, _, self.preprocess = open_clip.create_model_and_transforms("ViT-B-32")
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self.model.eval().cuda()
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# 预加载 L3 房间向量库
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self.room_uids, self.room_feats = [], []
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for uid, node in mem.nodes.items():
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if node.level == "L3" and node.clip_embedding is not None:
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self.room_uids.append(uid)
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self.room_feats.append(node.clip_embedding)
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self.room_feats = np.stack(self.room_feats).astype(np.float32)
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# 归一化
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self.room_feats /= np.linalg.norm(self.room_feats, axis=1, keepdims=True)
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@torch.no_grad()
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def __call__(self, zed_rgb: np.ndarray, top_k: int = 3) -> List[Tuple[str, float]]:
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from PIL import Image
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img = Image.fromarray(zed_rgb)
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x = self.preprocess(img).unsqueeze(0).cuda()
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feat = self.model.encode_image(x).cpu().numpy()[0]
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feat /= np.linalg.norm(feat)
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sims = self.room_feats @ feat # (N,)
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idx = np.argsort(-sims)[:top_k]
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return [(self.room_uids[i], float(sims[i])) for i in idx]
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```
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### 5.4.2 增强方案：DINOv2 / NetVLAD
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CLIP 在"语义相似但几何不同"（如所有酒店客房都长得差不多）时会混。增强做法：
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```python
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# 用 DINOv2 给出更强的"场景几何指纹"
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import dinov2
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class HybridCoarse:
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def __init__(self, mem):
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self.clip_r = CoarseRelocalizer(mem)
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self.dino = dinov2.load("dinov2_vitb14")
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# 建议在 ingest 阶段同时离线提 DINO 特征存进 node.attributes['dino_emb']
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...
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def __call__(self, rgb):
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clip_top = self.clip_r(rgb, top_k=5)
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# 再用 DINO 重排
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dino_feat = extract_dino(self.dino, rgb)
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reranked = []
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for uid, _ in clip_top:
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d = cosine(dino_feat, self.mem.nodes[uid].attributes['dino_emb'])
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reranked.append((uid, d))
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reranked.sort(key=lambda x: -x[1])
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return reranked[:3]
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```
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### 5.4.3 投票（取连续多帧增强稳定性）
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```python
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def coarse_with_voting(coarse_fn, zed_stream, window=5) -> str:
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votes = {}
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for _ in range(window):
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frame = zed_stream.get_rgb()
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for uid, sim in coarse_fn(frame, top_k=3):
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votes[uid] = votes.get(uid, 0) + sim
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return max(votes, key=votes.get)
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```
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---
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## 5.5 Stage 2 — 几何精配准
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### 5.5.1 流程
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```python
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# spatial_memory/relocalize_fine.py
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import open3d as o3d
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import numpy as np
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def fine_register(zed_depth, zed_intrinsics, candidate_room_uid,
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mem: SpatialMemory,
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voxel: float = 0.05) -> Tuple[np.ndarray, float]:
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# 1) ZED 帧 → 点云
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src = depth_to_pointcloud(zed_depth, zed_intrinsics)
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src = src.voxel_down_sample(voxel)
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src.estimate_normals()
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# 2) 房间 mesh 采样
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room_mesh_path = f"robot_memory/ltm/meshes/{candidate_room_uid}.glb" # 房间 mesh
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# 若按房间没单独 mesh，则用 global mesh + 房间多边形裁剪
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tgt = mesh_to_pointcloud(room_mesh_path, n=200_000)
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tgt = tgt.voxel_down_sample(voxel)
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tgt.estimate_normals()
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# 3) 全局粗配准 (TEASER++ 或 RANSAC+FPFH)
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src_fpfh = o3d.pipelines.registration.compute_fpfh_feature(
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src, o3d.geometry.KDTreeSearchParamHybrid(voxel*5, 100))
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tgt_fpfh = o3d.pipelines.registration.compute_fpfh_feature(
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tgt, o3d.geometry.KDTreeSearchParamHybrid(voxel*5, 100))
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result_ransac = o3d.pipelines.registration.registration_ransac_based_on_feature_matching(
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src, tgt, src_fpfh, tgt_fpfh, mutual_filter=True,
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max_correspondence_distance=voxel*1.5,
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estimation_method=o3d.pipelines.registration.TransformationEstimationPointToPoint(False),
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ransac_n=4,
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checkers=[o3d.pipelines.registration.CorrespondenceCheckerBasedOnEdgeLength(0.9),
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o3d.pipelines.registration.CorrespondenceCheckerBasedOnDistance(voxel*1.5)],
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criteria=o3d.pipelines.registration.RANSACConvergenceCriteria(100000, 0.999))
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# 4) ICP 精化
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result_icp = o3d.pipelines.registration.registration_icp(
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src, tgt, voxel*0.5, result_ransac.transformation,
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o3d.pipelines.registration.TransformationEstimationPointToPlane())
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return result_icp.transformation, result_icp.fitness
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```
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### 5.5.2 候选轮询
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```python
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def relocalize(zed_frame, mem) -> RelocalizeResult:
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coarse = CoarseRelocalizer(mem)
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candidates = coarse(zed_frame.rgb, top_k=3)
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best = None
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for room_uid, _ in candidates:
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T, fit = fine_register(zed_frame.depth, zed_frame.intrinsics,
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room_uid, mem)
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if best is None or fit > best.fitness:
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best = RelocalizeResult(T_zed_to_map=T, fitness=fit,
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room_uid=room_uid)
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if fit > 0.85: # 高质量提前退出
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break
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return best
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```
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### 5.5.3 接受 / 拒绝阈值
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| fitness | 行动 |
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|---------|------|
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| > 0.85 | 接受，直接发布 |
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| 0.70–0.85 | 接受但标记 `confidence=medium`，触发 5 s 内复检 |
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| 0.50–0.70 | 拒绝，换 anchor 级精配（5.6）再试 |
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| < 0.50 | 失败，进入人工兜底 |
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---
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## 5.6 Anchor 级超精配（对付 fitness 偏低）
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当房间级配准 fitness < 0.7，可能是因为房间太大、视场只看到局部。退化为 **anchor 级**：
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```python
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def fine_register_anchor(zed_pc, anchor: Anchor, mem) -> Tuple[np.ndarray, float]:
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node = mem.nodes[anchor.anchor_uid]
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# 取该 anchor 的局部 mesh（如这张床）
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tgt_pc = mesh_to_pointcloud(f"ltm/meshes/{node.uid}.glb", n=20_000)
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# 把 tgt 变换到 map 帧
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T_map = node.pose.to_matrix()
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tgt_pc = tgt_pc.transform(T_map)
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# 在 src 里**先用 YOLO 切出对应物体**，再 ICP
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src_pc = crop_pc_by_yolo_detection(zed_pc, label=node.label)
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if len(src_pc.points) < 500:
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return None, 0.0
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T_init = best_guess_init(node, zed_pose_estimate)
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T, fit = open3d_icp(src_pc, tgt_pc, T_init)
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return T, fit
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```
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→ 这种做法只用"床这个物体"对齐，比整个房间快也更鲁棒。
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---
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## 5.7 在线维护：连续 anchor 校正
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冷启动成功后，机器人开始巡逻。VIO 会慢慢漂，需要持续校正：
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```python
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class OnlineRelocalizer:
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def __init__(self, mem, vio):
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self.mem = mem
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self.vio = vio
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self.T_zed_to_map = np.eye(4) # 上次重定位结果
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self.last_correction_t = 0
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self.drift_estimate = 0.0 # 累计漂移估计
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def step(self, zed_frame):
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# 1. 取 ZED VIO 当前位姿（zed 帧）
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T_robot_zed = self.vio.current_pose()
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# 2. 全局位姿
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T_robot_map = self.T_zed_to_map @ T_robot_zed
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# 3. 用 YOLO 看当前帧有没有 anchor 类家具
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detections = yolo_world(zed_frame.rgb, classes=ANCHOR_LABELS)
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for det in detections:
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anchor = match_to_anchor(det, T_robot_map, self.mem)
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if anchor is None: continue
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# 4. 局部 ICP 校正
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T_new, fit = fine_register_anchor(zed_frame.pc, anchor, self.mem)
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if fit > 0.8:
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# 用新 T 替换全局
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self.T_zed_to_map = T_new @ np.linalg.inv(T_robot_zed)
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self.last_correction_t = time.time()
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self.drift_estimate = 0
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return
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# 没看到 anchor：用 IMU 估计漂移
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self.drift_estimate += self.vio.expected_drift_per_sec * dt
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if self.drift_estimate > 0.5: # 50 cm 漂移触发主动重定位
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self.trigger_full_relocalize()
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```
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---
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## 5.8 输出：ROS 2 TF 发布
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把握手结果发布到 TF 树，让导航栈/Agent 用：
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```python
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# nodes/relocalizer_node.py
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import rclpy
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from rclpy.node import Node
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from geometry_msgs.msg import TransformStamped
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from tf2_ros import StaticTransformBroadcaster
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class RelocalizerNode(Node):
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def __init__(self):
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super().__init__("prism_relocalizer")
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self.br = StaticTransformBroadcaster(self)
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def publish(self, T_zed_to_map: np.ndarray, stamp):
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msg = TransformStamped()
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msg.header.stamp = stamp
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msg.header.frame_id = "map"
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msg.child_frame_id = "zed2i_init" # 锚定 ZED 起点
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msg.transform.translation.x = float(T_zed_to_map[0,3])
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msg.transform.translation.y = float(T_zed_to_map[1,3])
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msg.transform.translation.z = float(T_zed_to_map[2,3])
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q = matrix_to_quat(T_zed_to_map[:3,:3])
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msg.transform.rotation.w, msg.transform.rotation.x, \
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msg.transform.rotation.y, msg.transform.rotation.z = q
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self.br.sendTransform(msg)
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```
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TF 树：
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```mermaid
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flowchart LR
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MAP["map"] --> INIT["zed2i_init<br/><i>(相对固定，<br/>重定位时刷新)</i>"] --> VIO["zed2i_camera (VIO)<br/><i>(30 Hz VIO 输出)</i>"] --> BASE["base_link"]
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style MAP fill:#fff7d6,stroke:#c97a00
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style INIT fill:#ffe9b3,stroke:#c97a00
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```
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---
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## 5.9 失败兜底（人工 / 半自动）
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如果 fitness 一直 < 0.5：
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| 兜底方式 | 操作 | 适用 |
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|----------|------|------|
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| **二维码兜底** | 在每个房间门口贴 1 个 ArUco（与 iPhone 扫描时的一致） | 永久可靠，建议默认配置 |
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| **遥控引导** | 人手柄遥控机器人到某 anchor 前，按"我在这"按钮 | 应急 |
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| **iPhone 联动** | 用员工的 iPhone 走到机器人旁，App 计算两者相对位姿 | 黑科技兜底 |
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---
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## 5.10 评测协议
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每次部署后跑一遍：
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| 测试 | 方法 | 通过标准 |
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|------|------|----------|
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| 冷启动重定位 | 机器人随机放在 10 个位置开机 | 9/10 成功，位置误差 < 15 cm |
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| 跨房间一致性 | 跑同一条路径 3 次，比较返回时位姿 | 漂移 < 20 cm |
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| 绑架恢复 | 机器人运行中被抱到另一房间放下 | 30 s 内恢复 |
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| 高反光区 | 把机器人放在卫生间镜前 | 应自动跳过镜面区做 ICP，不挂 |
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| 黑暗 | 关灯只留小夜灯 | CLIP 降级，仍能由几何 ICP 兜住 |
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---
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## 5.11 性能预算（Jetson Orin AGX）
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| 阶段 | 耗时 | GPU 占用 |
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|------|------|----------|
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| Stage 1 CLIP | 80 ms | 1.2 GB |
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| Stage 1 DINO 重排（可选） | +60 ms | 2 GB |
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| Stage 2 RANSAC+FPFH | 300 ms | 0 (CPU) |
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| Stage 2 ICP refine | 100 ms | 0 |
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| 总冷启动 | < 1 s | < 3 GB |
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| 在线 anchor 校正 | 200 ms / 触发 | 1 GB |
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---
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## 5.12 本章小结
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| 关键点 | 一句话 |
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|--------|--------|
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| **目的** | 算一个 `T_zed→map`，让 ZED 的所有后续输出与 iPhone LTM 同框 |
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| **策略** | 两段式：CLIP 粗匹配 → ICP 精配准 |
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| **效率** | 冷启动 < 1 s，在线 anchor 校正 < 200 ms |
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| **鲁棒** | 多帧投票 + 多 anchor 轮询 + ArUco 兜底 |
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| **维护** | 巡逻中持续看到 anchor 就刷新一次，对抗 VIO 长漂 |
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读完本章你应能：
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- ✅ 实现一个 ROS 2 节点，3 s 内告诉机器人"我在 305 房间床前"
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- ✅ 设计鲁棒性测试用例
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- ✅ 在 fitness 低时知道如何回退
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下一章 [`06_pipeline_C_online_perception.md`](06_pipeline_C_online_perception.md) 讲机器人已经定位后，怎么把在线观测**写回**记忆。
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---
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**章节版本**：v1.0
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**估计阅读时间**：18 分钟
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**关键收获**：从"我在哪都不知道"到"我精确在 map 帧 (1.23, 4.56, 0.0)"的完整握手流程
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