Scholarlink

<p>To address visual tracking drift caused by scale variation and occlusion, large errors of monocular ranging, and the insufficient obstacle-avoidance response in robot dynamic target following, this paper proposes a multimodal perception–based following framework. First, a KCF tracker is enhanced with a closed-loop scale-adaptive mechanism and a four-level occlusion handling scheme, so that robust target localization can still be maintained under severe appearance changes. Second, a camera-calibration–driven distance mapping function is constructed on top of the perspective projection model by introducing geometric distortion compensation, pose compensation, and a learnable bias term, which brings the monocular distance error down to the centimeter level within the 1–4 m working range. Third, LiDAR point cloud is used to build an obstacle threat model, and a semantic TEB local planner is employed to achieve coordinated target following and obstacle avoidance on a CPU-only mobile robot platform. Experiments on public long-term tracking benchmarks (LaSOT, TrackingNet, GOT-10k) and on an indoor TurtleBot3 platform show that, compared with the original KCF, the proposed method improves the LaSOT AUC from ~ 20% to 39.1% while keeping real-time performance, and, in real robot scenarios with occlusion, scale change, and dynamic obstacles, achieves a following success rate of 95.2% and a calibrated distance MAE of about 0.027 m (≈2.7%), demonstrating strong practicality and reproducibility.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A Multi-Module Perception-Based Robot Dynamic Target Following Method—Collaboration of Improved KCF Tracking, Distance Mapping Function, and Obstacle Avoidance

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月12日

查看全文
  • Xiaohong Lan,
  • Jian Zhang,
  • Miao Sun

摘要

To address visual tracking drift caused by scale variation and occlusion, large errors of monocular ranging, and the insufficient obstacle-avoidance response in robot dynamic target following, this paper proposes a multimodal perception–based following framework. First, a KCF tracker is enhanced with a closed-loop scale-adaptive mechanism and a four-level occlusion handling scheme, so that robust target localization can still be maintained under severe appearance changes. Second, a camera-calibration–driven distance mapping function is constructed on top of the perspective projection model by introducing geometric distortion compensation, pose compensation, and a learnable bias term, which brings the monocular distance error down to the centimeter level within the 1–4 m working range. Third, LiDAR point cloud is used to build an obstacle threat model, and a semantic TEB local planner is employed to achieve coordinated target following and obstacle avoidance on a CPU-only mobile robot platform. Experiments on public long-term tracking benchmarks (LaSOT, TrackingNet, GOT-10k) and on an indoor TurtleBot3 platform show that, compared with the original KCF, the proposed method improves the LaSOT AUC from ~ 20% to 39.1% while keeping real-time performance, and, in real robot scenarios with occlusion, scale change, and dynamic obstacles, achieves a following success rate of 95.2% and a calibrated distance MAE of about 0.027 m (≈2.7%), demonstrating strong practicality and reproducibility.