Rethinking Trajectory Evaluation for SLAM: a Probabilistic, Continuous-Time Approach

Abstract

Despite the existence of different error metrics for trajectory evaluation in SLAM, their theoretical justifications and connections are rarely studied, and few methods handle temporal association properly. In this work, we propose to formulate the trajectory evaluation problem in a probabilistic, continuous-time framework. By modeling the groundtruth as random variables, the concepts of absolute and relative error are generalized to be likelihood. Moreover, the groundtruth is represented as a piecewise Gaussian Process in continuous-time. Within this framework, we are able to establish theoretical connections between relative and absolute error metrics and handle temporal association in a principled manner.Comment: Accepted at ICRA19 Workshop on Dataset Generation and Benchmarking of SLAM Algorithms for Robotics and VR/AR. Best paper awar