An evidence‐based approach to object pose estimation from LiDAR measurements in challenging environments

Abstract

Abstract This paper addresses the problem of estimating object pose from high‐density LiDAR measurements in unpredictable field robotic environments. Point‐cloud measurements collected in such environments do not lend themselves to providing an initial estimate or systematic segmentation of the point‐cloud. A novel approach is presented that evaluates measurements individually for the evidence they provide to a collection of pose hypotheses. A maximum evidence strategy is constructed that is based in the idea that the most likely pose must be that which is most consistent with the observed LiDAR range measurements. This evidence‐based approach is shown to handle the diversity of range measurements without an initial estimate or segmentation. The method is robust to dust. The approach is demonstrated by two pose estimation problems associated with the automation of a large mining excavator.