FFI-VTR：基于特征流指示器与概率运动规划的轻量级鲁棒视觉示教与重复导航
FFI-VTR: Lightweight and Robust Visual Teach and Repeat Navigation based on Feature Flow Indicator and Probabilistic Motion Planning
- Department of Automation, University of Science and Technology of China (USTC)（自动化系，中国科学技术大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出FFI-VTR方法，通过特征流与运动的定性映射和概率运动规划，实现无需精确定位与稠密重建的轻量鲁棒视觉示教与重复导航。
AI中文摘要：
尽管视觉与重复导航是移动机器人自主导航的一种便捷解决方案，但在任务环境中实现效率与鲁棒性之间的平衡仍然面临挑战。本文提出了一种新颖的视觉与重复机器人自主导航方法，该方法无需精确的定位和稠密重建模块，从而使我们的系统具有轻量化和鲁棒性的特点。首先，引入特征流，并建立了特征流与机器人运动之间的定性映射关系，其中特征流被定义为匹配特征之间的像素位置偏差。基于该映射模型，示教阶段输出的地图被表示为一个关键帧图，图中边上的特征流编码了相邻关键帧之间的相对运动。其次，视觉重复导航本质上被建模为当前观测与地图关键帧之间的特征流最小化问题。为了驱动机器人在没有精确的定位的情况下持续减小当前帧与地图关键帧之间的特征流，基于我们的定性特征流-运动映射指示器开发了一种概率运动规划方法。使用我们的移动平台进行的大量实验表明，我们提出的方法轻量、鲁棒，并且优于基线方法。源代码已在 https://github.com/wangjks/FFI-VTR 公开，以惠及社区。
英文摘要：
Though visual and repeat navigation is a convenient solution for mobile robot self-navigation, achieving balance between efficiency and robustness in task environment still remains challenges. In this paper, we propose a novel visual and repeat robotic autonomous navigation method that requires no accurate localization and dense reconstruction modules, which makes our system featured by lightweight and robustness. Firstly, feature flow is introduced and we develop a qualitative mapping between feature flow and robot's motion, in which feature flow is defined as pixel location bias between matched features. Based on the mapping model, the map outputted by the teaching phase is represented as a keyframe graph, in which the feature flow on the edge encodes the relative motion between adjacent keyframes. Secondly, the visual repeating navigation is essentially modeled as a feature flow minimization problem between current observation and the map keyframe. To drive the robot to consistently reduce the feature flow between current frame and map keyframes without accurate localization, a probabilistic motion planning is developed based on our qualitative feature flow-motion mapping indicator. Extensive experiments using our mobile platform demonstrates that our proposed method is lightweight, robust, and superior to baselines. The source code has been made public at https://github.com/wangjks/FFI-VTR to benefit the community.
