提出两种高效算法，实时筛选对定位最有用的视觉特征。
Efficient Greedy Algorithms for Feature Selection in Robot Visual Localization
- 基于贪心策略，快速评估特征对定位的贡献度。
- 显著降低计算与内存开销，保持定位精度。
- 适合资源受限的机器人实时系统使用。
机器人定位是未知环境中自主导航的基础。视觉传感器（如摄像头）通过追踪图像帧间的点特征来估计位置，但图像中常包含大量冗余或无信息的特征，处理全部特征会引入显著的计算延迟和效率问题。为此，本文提出两种快速、低内存消耗的特征选择算法，使机器人能够实时主动评估视觉特征的有用性。与现有方法相比，所提方法在降低时间与内存复杂度的同时，实现了计算效率与定位精度的良好平衡。
原文摘要 · Abstract (English)
Robot localization is a fundamental component of autonomous navigation in unknown environments. Among various sensing modalities, visual input from cameras plays a central role, enabling robots to estimate their position by tracking point features across image frames. However, image frames often contain a large number of features, many of which are redundant or uninformative for localization. Processing all features can introduce significant computational latency and inefficiency. This motivates the need for intelligent feature selection, identifying a subset of features that are most informative for localization over a prediction horizon. In this work, we propose two fast and memory-efficient feature selection algorithms that enable robots to actively evaluate the utility of visual features in real time. Unlike existing approaches with high computational and memory demands, the proposed methods are explicitly designed to reduce both time and memory complexity while achieving a favorable trade-off between computational efficiency and localization accuracy.
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