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This paper benchmarks visual odometry (VO) and visual SLAM (VSLAM) systems for mobile robot navigation in logistics environments, evaluating performance against ground truth from Vicon and LiDAR-SLAM. The study finds that a hybrid approach, using the cuVSLAM front-end with a custom SLAM back-end, yields the best mapping accuracy. The integrated cuVSLAM-based VO stack is validated through deployment on an NVIDIA Jetson platform.
A hybrid cuVSLAM-based visual SLAM system achieves superior mapping accuracy in real-world logistics environments, outperforming other VO/VSLAM approaches.
This work presents a comprehensive benchmark evaluation of visual odometry (VO) and visual SLAM (VSLAM) systems for mobile robot navigation in real-world logistical environments. We compare multiple visual odometry approaches across controlled trajectories covering translational, rotational, and mixed motion patterns, as well as a large-scale production facility dataset spanning approximately 1.7 km. Performance is evaluated using Absolute Pose Error (APE) against ground truth from a Vicon motion capture system and a LiDAR-based SLAM reference. Our results show that a hybrid stack combining the cuVSLAM front-end with a custom SLAM back-end achieves the strongest mapping accuracy, motivating a deeper integration of cuVSLAM as the core VO component in our robotics stack. We further validate this integration by deploying and testing the cuVSLAM-based VO stack on an NVIDIA Jetson platform.
NVIDIA