Inference on networks of mixtures for robust robot mapping

摘要

The central challenge in robotic mapping is obtaining reliable data associations (or "loop closures"): state-ofthe-art inference algorithms can fail catastrophically if even one erroneous loop closure is incorporated into the map.Consequently, much work has been done to push error rates closer to zero.However, a long-lived or multi-robot system will still encounter errors, leading to system failure.We propose a fundamentally different approach: allow richer error models that allow the probability of a failure to be explicitly modeled.In other words, we optimize the map while simultaneously determining which loop closures are correct from within a single integrated Bayesian framework.Unlike earlier multiple-hypothesis approaches, our approach avoids exponential memory complexity and is fast enough for realtime performance.We show that the proposed method not only allows loop closing errors to be automatically identified, but also that in extreme cases, the "front-end" loop-validation systems can be unnecessary.We demonstrate our system both on standard benchmarks and on the real-world datasets that motivated this work.