Navigation and mapping for robots require data fusion from various sensors, each producing uncertain and opportunistic measurement data.
We are continuing with a multi-year, native Julia factor graph based simultaneous localization and mapping (SLAM) inference system that grew out of research work on non-Gaussian state-estimation, and is the primary implementation of the "multimodal-iSAM" algorithm from robotics literature.