  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.

Robotics