VINSEval: Evaluation Framework for Unified Testing of Consistency and Robustness of Visual-Inertial Navigation System Algorithms

The research community presented significant advances in many different Visual-Inertial Navigation System (VINS) algorithms to localize mobile robots or hand-held devices in a 3D environment. While authors of the algorithms often do compare to, at that time, existing competing approaches, their comparison methods, rigor, depth, and repeatability at later points in time have a large spread. Further, with existing simulators and photo-realistic frameworks, the user is not able to easily test the sensitivity of the algorithm under examination with respect to specific environmental conditions and sensor specifications. Rather, tests often include unwillingly many polluting effects falsifying the analysis and interpretations. In addition, edge cases and corresponding failure modes often remain undiscovered due to the limited breadth of the test sequences. Our unified evaluation framework allows, in a fully automated fashion, a reproducible analysis of different VINS methods with respect to specific environmental and sensor parameters. The analyses per parameter are done over a multitude of test sets to obtain both statistically valid results and an average over other, potentially polluting effects with respect to the one parameter under test to mitigate biased interpretations. The automated performance results per method over all tested parameters are then summarized in unified radar charts for a fair comparison across authors and institutions.