Efficient Greedy Algorithms for Feature Selection in Robot Visual Localization

TL;DR

This paper introduces two efficient algorithms for real-time feature selection in robot visual localization, significantly reducing computational and memory requirements while maintaining high localization accuracy.

Contribution

The paper presents novel, fast, and memory-efficient feature selection algorithms tailored for real-time robot localization using visual data.

Findings

Algorithms outperform existing methods in speed and memory usage.

Localization accuracy is maintained with fewer features.

Real-time implementation is feasible on resource-constrained robots.

Abstract

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.