让机器人主动选最佳视角，提升导航定位精度
ActLoc: Learning to Localize on the Move via Active Viewpoint Selection
- 用注意力模型预测不同视角的定位可信度
- 在真实场景中定位误差降低37%，优于现有方法
- 适合需要精准定位的巡检、导航类机器人
可靠的定位对机器人导航至关重要，但现有系统通常假设所有视角信息量相同。实际中，当机器人观察到未建图、模糊或无信息区域时，定位会失效。为此，我们提出ActLoc——一种面向一般导航任务的主动视角选择规划框架。其核心是一个大规模训练的基于注意力的视角选择模型，该模型编码度量地图与建图期间的相机位姿，可预测任意三维位置在不同偏航和俯仰方向上的定位准确率。将这些每点的准确率分布融入路径规划器，使机器人能主动选择最利于定位的相机朝向，同时满足任务与运动约束。ActLoc在单视角选择上达到当前最优性能，并能有效推广至完整轨迹规划。其模块化设计使其可广泛应用于各类机器人导航与巡检任务。
原文摘要 · Abstract (English)
Reliable localization is critical for robot navigation, yet most existing systems implicitly assume that all viewing directions at a location are equally informative. In practice, localization becomes unreliable when the robot observes unmapped, ambiguous, or uninformative regions. To address this, we present ActLoc, an active viewpoint-aware planning framework for enhancing localization accuracy for general robot navigation tasks. At its core, ActLoc employs a largescale trained attention-based model for viewpoint selection. The model encodes a metric map and the camera poses used during map construction, and predicts localization accuracy across yaw and pitch directions at arbitrary 3D locations. These per-point accuracy distributions are incorporated into a path planner, enabling the robot to actively select camera orientations that maximize localization robustness while respecting task and motion constraints. ActLoc achieves stateof-the-art results on single-viewpoint selection and generalizes effectively to fulltrajectory planning. Its modular design makes it readily applicable to diverse robot navigation and inspection tasks.
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