Metrics for Real-Time Mono-VSLAM Evalua...

Vision based algorithms became popular for state estimation and subsequent (local) control of mobile robots. Currently a large variety of such algorithms exists and their performance is often characterized through their drift relative to the total trajectory traveled. However, this metric has relatively low relevance for local vehicle control/stabilization. In this paper, we propose a set of metrics which allows to evaluate a vision based algorithm with respect to its usability for state estimation and subsequent (local) control of highly dynamic autonomous mobile platforms such as multirotor UAVs. As such platforms usually make use of inertial measurements to mitigate the relatively low update rate of the visual algorithm, we particularly focus on a new metric taking the expected IMU-induced drift between visual readings into consideration based on the probabilistic properties of the sensor. We demonstrate this set of metrics by comparing ORB-SLAM, LSD-SLAM and DSO on different datasets.