Rethinking Trajectory Evaluation for SLAM: a Probabilistic, Continuous-Time Approach
This work addresses trajectory evaluation for SLAM researchers, offering a principled approach to improve accuracy and consistency in benchmarking.
The paper tackles the problem of evaluating SLAM trajectories by proposing a probabilistic, continuous-time framework that generalizes error metrics to likelihood and handles temporal association, establishing theoretical connections between absolute and relative errors.
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.