Industrial cuVSLAM Benchmark & Integration
This addresses the problem of accurate navigation for mobile robots in industrial settings, but it is incremental as it focuses on benchmarking and integrating existing methods.
This work benchmarks visual odometry and SLAM systems for mobile robots in logistical environments, finding that a hybrid stack with cuVSLAM front-end achieves the strongest mapping accuracy, motivating its integration into a robotics stack tested on an NVIDIA Jetson platform.
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.