A Comparison of Modern General-Purpose Visual SLAM Approaches
It addresses the need for robust visual SLAM systems to replace traditional 2D lidar in diverse environments for service and consumer robots, but it is incremental as it focuses on benchmarking existing methods.
This paper compares three modern visual SLAM approaches (ORB-SLAM3, OpenVSLAM, and RTABMap) across multiple datasets to identify general-purpose options for service robots, finding that ORB-SLAM3 and OpenVSLAM had not been benchmarked against some datasets previously.
Advancing maturity in mobile and legged robotics technologies is changing the landscapes where robots are being deployed and found. This innovation calls for a transformation in simultaneous localization and mapping (SLAM) systems to support this new generation of service and consumer robots. No longer can traditionally robust 2D lidar systems dominate while robots are being deployed in multi-story indoor, outdoor unstructured, and urban domains with increasingly inexpensive stereo and RGB-D cameras. Visual SLAM (VSLAM) systems have been a topic of study for decades and a small number of openly available implementations have stood out: ORB-SLAM3, OpenVSLAM and RTABMap. This paper presents a comparison of these 3 modern, feature rich, and uniquely robust VSLAM techniques that have yet to be benchmarked against each other, using several different datasets spanning multiple domains negotiated by service robots. ORB-SLAM3 and OpenVSLAM each were not compared against at least one of these datasets previously in literature and we provide insight through this lens. This analysis is motivated to find general purpose, feature complete, and multi-domain VSLAM options to support a broad class of robot applications for integration into the new and improved ROS 2 Nav2 System as suitable alternatives to traditional 2D lidar solutions.