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.

翻译：大规模环境下的鲁棒SLAM需要在多个阶段（从感知、里程计估计到回环闭合）具备容错能力和感知能力。本文提出TBV（Trust But Verify）雷达SLAM，一种通过内省验证回环闭合候选的雷达SLAM方法。TBV雷达SLAM通过结合多种地点识别技术实现了高正确回环检索率：紧耦合的地点相似度与里程计不确定性搜索、通过原点偏移扫描生成回环描述子，以及将回环选择延迟至验证之后。对虚假约束的鲁棒性通过仔细验证并筛选多个回环约束中最可能的候选来实现。关键在于，验证和筛选在配准之后进行，此时可以轻松计算回环证据的额外来源。我们将回环检索与验证方法集成到一个位姿图框架内的容错里程计管道中。通过在公共基准上的评估，我们发现TBV雷达SLAM的误差比先前最先进方法降低了65%。我们还证明，该方法无需调整任何参数即可跨环境泛化。