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

The use of Autonomous Mobile Robots (AMR) plays asignificant role inthe automation of intralogisticsprocesses. For safe operation and navigation, high locali-zation accuracy is required. Common AMR systems rely oncost-intensive sensors such as LIDAR scanners. To en-able widespread use of AMRs the industry alternative so-lutions are required. This study explores stereo camera-based visual SLAM as a cost-effective alternative to con-ventional 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 0.17m and an RPE of 0.02m while the use of a RealSense D455 showed an APE of 0.33m with an RPE of 0.02m