Predicting Performance of SLAM Algorithms

Understand

Among the abilities that autonomous mobile robots should exhibit, map building and localization are definitely recognized as fundamental.

• Consequently, countless algorithms for solving the Simultaneous Localization And Mapping (SLAM) problem have been proposed.
• Currently, their evaluation is performed ex-post, according to outcomes obtained when running the algorithms on data collected by robots in real or simulated environments.
• In this paper, we present a novel method that allows the ex-ante prediction of the performance of a SLAM algorithm in an unseen environment, before it is actually run.