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Industrial cuVSLAM Benchmark & Integration

Charbel Abi Hana, Kameel Amareen, Mohamad Mostafa, Dmitry Slepichev, Hesam Rabeti, Zheng Wang, Mihir Acharya, Anthony Rizk

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.

Industrial cuVSLAM Benchmark & Integration

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.
Paper Structure (16 sections, 1 equation, 4 figures, 5 tables)

This paper contains 16 sections, 1 equation, 4 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Benchmarked Systems and Evaluation Criteria
  3. Benchmarked Systems
  4. ORB-SLAM3
  5. RTAB-Map
  6. cuVSLAM
  7. Evaluation Metric
  8. Visual Odometry Benchmark
  9. Benchmark Dataset
  10. Experimental Setup
  11. Results and Analysis
  12. Visual SLAM Benchmark
  13. Dataset
  14. Experimental Setup
  15. Results and Analysis
  16. ...and 1 more sections

Figures (4)

  • Figure 1: VO trajectories (from left to right): L-shape, hybrid & rotation-heavy sequences.
  • Figure 2: Radar plot comparing the metrics of each method across the three VO sequences. The wheel encoder baseline is included for reference. Smaller areas indicate better performance.
  • Figure 3: Estimated trajectory errors on Seq-570.
  • Figure 4: Estimated trajectory errors on Seq-1700.