FLAF：用于视觉示教与重复的焦线与特征约束主动视图规划
FLAF: Focal Line and Feature-constrained Active View Planning for Visual Teach and Repeat
- Shenzhen Key Laboratory of Robotics and Computer Vision（深圳机器人与计算机视觉重点实验室）
- Southern University of Science and Technology（南方科技大学）
- Department of Electrical and Electronic Engineering（电子与电气工程系）
- Peng Cheng National Laboratory（鹏城实验室）
- Guangdong University of Technology（广东工业大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出FLAF主动视图规划方法，集成于视觉SLAM构建主动VT&R系统，通过引导主动相机朝向更多可识别地图点，有效解决无纹理环境下的跟踪失败问题。
AI中文摘要：
本文提出了FLAF，一种焦线与特征约束的主动视图规划方法，用于避免移动机器人基于特征视觉导航中的跟踪失败。基于FLAF的视觉导航建立在基于特征的视觉示教与重复（VT&R）框架之上，通过引导机器人在覆盖日常自主导航需求重要部分的各类路径上行驶，支持多种机器人应用。然而，人造环境中无纹理区域导致的基于特征视觉同步定位与建图（VSLAM）跟踪失败，仍限制了VT&R在现实世界中的应用。为解决此问题，所提视图规划器被集成到基于特征的视觉SLAM系统中，构建出避免跟踪失败的主动VT&R系统。该系统中，移动机器人搭载了基于云台（PTU）的主动相机。使用FLAF，基于主动相机的VSLAM在示教阶段运行以构建完整路径地图，并在重复阶段维持稳定定位。FLAF引导机器人朝向更多地图点以避免路径学习中的建图失败，并在沿学习轨迹行驶时朝向更多有利于定位的特征可识别地图点。真实场景实验表明，FLAF优于未考虑特征可识别性的方法，且该主动VT&R系统通过有效处理低纹理区域在复杂环境中表现良好。
英文摘要：
This paper presents FLAF, a focal line and feature-constrained active view planning method for tracking failure avoidance in feature-based visual navigation of mobile robots. Our FLAF-based visual navigation is built upon a feature-based visual teach and repeat (VT\&R) framework, which supports many robotic applications by teaching a robot to navigate on various paths that cover a significant portion of daily autonomous navigation requirements. However, tracking failure in feature-based visual simultaneous localization and mapping (VSLAM) caused by textureless regions in human-made environments is still limiting VT\&R to be adopted in the real world. To address this problem, the proposed view planner is integrated into a feature-based visual SLAM system to build up an active VT\&R system that avoids tracking failure. In our system, a pan-tilt unit (PTU)-based active camera is mounted on the mobile robot. Using FLAF, the active camera-based VSLAM operates during the teaching phase to construct a complete path map and in the repeat phase to maintain stable localization. FLAF orients the robot toward more map points to avoid mapping failures during path learning and toward more feature-identifiable map points beneficial for localization while following the learned trajectory. Experiments in real scenarios demonstrate that FLAF outperforms the methods that do not consider feature-identifiability, and our active VT\&R system performs well in complex environments by effectively dealing with low-texture regions.
