动态环境中，自动适应未知移动物体的视觉定位系统。
VAR-SLAM: Visual Adaptive and Robust SLAM for Dynamic Environments
- 用轻量语义关键点过滤已知移动物体，自适应鲁棒损失处理未知物体。
- 在线估计鲁棒核形状参数，使系统在高斯与重尾行为间自动切换。
- 在多个数据集上比先进方法轨迹误差低25%，实时运行达27帧/秒。
动态环境中的视觉SLAM仍具挑战性，因现有方法依赖仅处理已知类别物体的语义过滤，或使用固定鲁棒核，无法适应未知移动物体，导致其出现时精度下降。我们提出VAR-SLAM（视觉自适应鲁棒SLAM），基于ORB-SLAM3架构，结合轻量语义关键点滤波器处理已知移动物体，以及Barron自适应鲁棒损失处理未知物体。鲁棒核的形状参数通过残差在线估计，使系统可自动在高斯与重尾行为间调整。我们在TUM RGB-D、Bonn RGB-D Dynamic和OpenLORIS数据集上评估，这些数据集包含已知与未知移动物体。结果表明，相比最先进基线，轨迹精度与鲁棒性均有提升，在挑战性序列上相较NGD-SLAM ATE RMSE降低最高达25%，同时平均保持27 FPS的运行性能。
原文摘要 · Abstract (English)
Visual SLAM in dynamic environments remains challenging, as several existing methods rely on semantic filtering that only handles known object classes, or use fixed robust kernels that cannot adapt to unknown moving objects, leading to degraded accuracy when they appear in the scene. We present VAR-SLAM (Visual Adaptive and Robust SLAM), an ORB-SLAM3-based system that combines a lightweight semantic keypoint filter to deal with known moving objects, with Barron's adaptive robust loss to handle unknown ones. The shape parameter of the robust kernel is estimated online from residuals, allowing the system to automatically adjust between Gaussian and heavy-tailed behavior. We evaluate VAR-SLAM on the TUM RGB-D, Bonn RGB-D Dynamic, and OpenLORIS datasets, which include both known and unknown moving objects. Results show improved trajectory accuracy and robustness over state-of-the-art baselines, achieving up to 25% lower ATE RMSE than NGD-SLAM on challenging sequences, while maintaining performance at 27 FPS on average.
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