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.
翻译：移动和脚步机器人技术的成熟程度正在改变机器人部署和发现的地貌。这一创新要求同时改造本地化和绘图系统(SLAM)以支持新一代服务和消费机器人。在机器人被部署在多层室内、室外无结构的多层楼层和城市域,立体立体立体和RGB-D照相机越来越便宜的情况下,传统的2D里拉达系统不再能够主宰传统的2D系统。视觉SLAM(VSLAM)系统几十年来一直是研究的课题,只有少数公开可用的实施项目已经出现:ORB-SLAM3、OpenVSLAM和RTABmap。本文比较了这三种现代的、丰富和独特强健的VSLAM技术,这些技术尚未相互参照,同时使用服务机器人谈判的多层、多层、多层、多层、多层、多层、多层、多层、多层、多层、多层、多层、多层、多层、多层、多层、多层、多层、多层、多层、多层、多层、多层、多层、多层、多层、多层、多层、多层、多层、多层、多层、多层、多层、多层、多层、多层、多层、多层、多层、多层、多层、二、多层、多层、多层、多层、多层、多层、多层、多层、多层、二、二、二、二、二等、二层、二等,支持选项,以、二等等等等等等,支持了,以、二等等、多、多、二等、多、二等等等、多、多、多、多、多、多、多、多、多、多、多、多、多、多、多、多、多、多、多、多、多、多、多、多、多、多、多、多、多、多、多、多、多、多、多、多、多、多、多、多、多、多、多、多、多、多、多、多、多、多、多、多、多、多、多、多、多、多、多、多、多、多、多、多、多
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