3 accepted papers
SLAM is an important capability for many autonomous systems, and modern LiDAR-based methods offer promising performance. However, for long duration missions, existing works that either take directly the full pointclouds or extracted features face key tradeoffs in accuracy and computational efficienc
This work proposes a resilient and adaptive state estimation framework for robots operating in perceptually-degraded environments. The approach, called Adaptive Maximum Correntropy Criterion Kalman Filtering (AMCCKF), is inherently robust to corrupted measurements, such as those containing jumps or
Many robotic missions require online estimation of the unknown state transition models associated with uncertainty that stems from mission dynamics. The learning problem is usually distributed among agents in multiagent scenarios, either due to the absence of a centralized processing unit or because…