A Framework For Evaluating Visual SLAM

Abstract

Performance analysis in the field of camera-based simultaneous localisation and mapping (Visual SLAM, VSLAM) is still an unsolved problem. For VSLAM systems, there is a lack of generally accepted performance measures, test frameworks, and benchmark problems. Most researchers test by visually inspecting their systems on recorded image sequences, or measuring accuracy on simulated data of simplified point-cloud-like environments. Both approaches have their disadvantages. Recorded sequences lack ground truth. Simulations tend to oversimplify low-level aspects of the problem. In this paper, we propose to evaluate VSLAM systems on rendered image sequences. The intention is to move simulations towards more realistic conditions while still having ground truth. For this purpose, we provide a complete and extensible framework which addresses all aspects, from rendering to ground truth generation and automated evaluation. To illustrate the usefulness of this framework, we provide experimental results assessing the benefit of feature normal estimation and subpixel accurate matching on sequences with and without motion blur.