评测工业场景下VO/VSLAM系统，发现cuVSLAM融合方案精度最优。
Industrial cuVSLAM Benchmark & Integration
- 用真实物流环境数据对比多种视觉里程计方法
- 混合架构在1.7公里厂区中误差最低，优于基准
- 已部署至Jetson平台，适合工业机器人定位
本工作针对移动机器人在真实物流环境中的视觉里程计（VO）与视觉SLAM（VSLAM）系统，构建了全面的基准评估。在包含平移、旋转及复合运动模式的受控轨迹上进行对比，并基于约1.7公里大型生产厂区数据集进行测试。性能通过与Vicon运动捕捉系统和基于激光雷达的SLAM参考的绝对位姿误差（APE）进行评估。结果表明，采用cuVSLAM前端与自定义后端构成的混合架构，在建图精度上表现最佳，推动将cuVSLAM作为机器人系统核心视觉里程计组件的深度集成。进一步在NVIDIA Jetson平台上完成部署与验证。
原文摘要 · Abstract (English)
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.
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