Analysing visual-inertial odometry algorithms for the localization of industrial autonomous mobile robots in intralogistics and manufacturing

Abstract

The use of Autonomous Mobile Robots (AMR) plays a significant role in the automation of intralogistics processes. For safe operation and navigation, high localization accuracy is required. Common AMR systems rely on cost-intensive sensors such as LIDAR scanners. To enable widespread use of AMRs the industry alternative solutions are required. This study explores stereo camera based visual SLAM as a cost-effective alternative to conventional 3D LIDAR-based localization solutions for an industrial robot application. Using Stereolabs ZED 2I and Intel RealSense D455 cameras with ORB-SLAM3 and OpenVINS algorithms, we evaluated Mean Absolute Pose Error (APE) and Root Mean Square Pose Error (RPE). The highest accuracy was achieved with the ZED 2I with OpenVINS with an APE of 0.17 m and an RPE of 0.02 m while the use of a RealSense D455 showed an APE of 0.33 m with an RPE of 0.02 m.