TBV雷达SLAM——信任但验证回环候选
TBV Radar SLAM -- trust but verify loop candidates
AI总结：
TBV雷达SLAM通过内省式验证回环候选、结合地点相似性与里程计不确定性搜索及配准后验证选择，在公开基准上实现比现有最优方法低65%的误差，且无需调参即可跨环境泛化。
摘要：
大规模环境中的鲁棒SLAM需要在多个阶段具备容错能力和感知能力，从传感、里程计估计到回环闭合。在这项工作中，我们提出了TBV（Trust But Verify，信任但验证）雷达SLAM，一种对回环候选进行内省式验证的雷达SLAM方法。TBV雷达SLAM通过结合多种地点识别技术实现了较高的正确回环检索率：紧耦合的地点相似性与里程计不确定性搜索、从原点平移扫描生成回环描述子，以及将回环选择推迟到验证之后。通过对多个回环约束进行仔细验证并选择最可能的约束，实现了对错误约束的鲁棒性。重要的是，验证和选择是在配准之后进行的，此时可以容易地计算额外的回环证据来源。我们将回环检索和验证方法与容错里程计流程集成在位姿图框架中。通过在公开基准上的评估，我们发现TBV雷达SLAM的误差比之前的最先进方法低65%。我们还表明，该方法能够跨环境泛化，无需更改任何参数。
英文摘要：
Robust SLAM in large-scale environments requires fault resilience and awareness at multiple stages, from sensing and odometry estimation to loop closure. In this work, we present TBV (Trust But Verify) Radar SLAM, a method for radar SLAM that introspectively verifies loop closure candidates. TBV Radar SLAM achieves a high correct-loop-retrieval rate by combining multiple place-recognition techniques: tightly coupled place similarity and odometry uncertainty search, creating loop descriptors from origin-shifted scans, and delaying loop selection until after verification. Robustness to false constraints is achieved by carefully verifying and selecting the most likely ones from multiple loop constraints. Importantly, the verification and selection are carried out after registration when additional sources of loop evidence can easily be computed. We integrate our loop retrieval and verification method with a fault-resilient odometry pipeline within a pose graph framework. By evaluating on public benchmarks we found that TBV Radar SLAM achieves 65% lower error than the previous state of the art. We also show that it's generalizing across environments without needing to change any parameters.
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