At the core of most autonomous systems is a source of navigation data that provides position and orientation which often depends on a single state estimation system. This single-point of failure reduces robustness and reliability. Prior work in SLAM often focusses on accuracy. In this talk we will explore several ideas to improve the robustness of SLAM systems while maintaining high accuracy and mapping resolution. We present the challenges, as well as results of operating in dust, fog, high-resolution mapping, and robust place recognition in a variety of situations with traditional as well as learning-based approaches to SLAM.
Event is a WebEx presentation on October 28th. Starts at 5:15 pm
See link below to register and for more information.