Industrial cuVSLAM Benchmark & Integration

TL;DR

This paper evaluates various visual odometry and SLAM systems in real-world logistical environments, demonstrating that a hybrid cuVSLAM approach offers superior accuracy and effective deployment on embedded platforms.

Contribution

It provides a comprehensive benchmark of VO and VSLAM methods in logistical settings and introduces a hybrid cuVSLAM system with improved accuracy and integration for mobile robots.

Findings

Hybrid cuVSLAM with custom back-end outperforms other methods in accuracy.

The integrated system is successfully deployed on NVIDIA Jetson platform.

Benchmark results highlight strengths and limitations of different VO approaches.

Abstract

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.