Visual place recognition (VPR) is an essential component of robot navigation and localization systems that allows them to identify a place using only image data. VPR is challenging due to the significant changes in a place's appearance under different illumination throughout the day, with seasonal weather and when observed from different viewpoints. Currently, no single VPR technique excels in every environmental condition, each exhibiting unique benefits and shortcomings. As a result, VPR systems combining multiple techniques achieve more reliable VPR performance in changing environments, at the cost of higher computational loads. Addressing this shortcoming, we propose an adaptive VPR system dubbed Adaptive Multi-Self Identification and Correction (A-MuSIC). We start by developing a method to collect information of the runtime performance of a VPR technique by analysing the frame-to-frame continuity of matched queries. We then demonstrate how to operate the method on a static ensemble of techniques, generating data on which techniques are contributing the most for the current environment. A-MuSIC uses the collected information to both select a minimal subset of techniques and to decide when a re-selection is required during navigation. A-MuSIC matches or beats state-of-the-art VPR performance across all tested benchmark datasets while maintaining its computational load on par with individual techniques.

翻译：视觉地点识别（VPR）是机器人导航与定位系统的关键组成部分，使其能仅凭图像数据识别位置。由于不同光照条件（昼夜变化）、季节天气及视角改变会导致地点外观发生显著变化，VPR面临严峻挑战。当前，尚无单一VPR技术在所有环境条件下表现卓越，每种方法均具有独特优势与缺陷。因此，结合多种技术的VPR系统能在变化环境中实现更可靠的VPR性能，但代价是更高的计算负载。为解决此缺陷，我们提出自适应VPR系统——自适应多自识别与校正（A-MuSIC）。首先开发了一种方法，通过分析匹配查询的帧间连续性来收集VPR技术的运行时性能信息；随后阐述如何将该方法应用于静态技术集成系统，生成当前环境下各技术贡献度的数据。A-MuSIC利用收集的信息选择最小技术子集，并在导航过程中决定何时需要重新选择。在全部测试基准数据集上，A-MuSIC达到或超越最先进VPR性能，同时将计算负载维持在单一技术相当的水平。