{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T18:20:10Z","timestamp":1770747610899,"version":"3.49.0"},"reference-count":52,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2017,4,17]],"date-time":"2017-04-17T00:00:00Z","timestamp":1492387200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Robotics"],"abstract":"<jats:p>Place recognition is an essential component of autonomous mobile robot navigation. It is used for loop-closure detection to maintain consistent maps, or to localize the robot along a route, or in kidnapped-robot situations. Camera sensors provide rich visual information for this task. We compare different approaches for visual place recognition: holistic methods (visual compass and warping), signature-based methods (using Fourier coefficients or feature descriptors (able for binary-appearance loop-closure evaluation, ABLE)), and feature-based methods (fast appearance-based mapping, FabMap). As new contributions we investigate whether warping, a successful visual homing method, is suitable for place recognition. In addition, we extend the well-known visual compass to use multiple scale planes, a concept also employed by warping. To achieve tolerance against changing illumination conditions, we examine the NSAD distance measure (normalized sum of absolute differences) on edge-filtered images. To reduce the impact of illumination changes on the distance values, we suggest to compute ratios of image distances to normalize these values to a common range. We test all methods on multiple indoor databases, as well as a small outdoor database, using images with constant or changing illumination conditions. ROC analysis (receiver-operator characteristics) and the metric distance between best-matching image pairs are used as evaluation measures. Most methods perform well under constant illumination conditions, but fail under changing illumination. The visual compass using the NSAD measure on edge-filtered images with multiple scale planes, while being slower than signature methods, performs best in the latter case.<\/jats:p>","DOI":"10.3390\/robotics6020009","type":"journal-article","created":{"date-parts":[[2017,4,18]],"date-time":"2017-04-18T11:22:04Z","timestamp":1492514524000},"page":"9","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Visual Place Recognition for Autonomous Mobile Robots"],"prefix":"10.3390","volume":"6","author":[{"given":"Michael","family":"Horst","sequence":"first","affiliation":[{"name":"Computer Engineering Group, Faculty of Technology, Bielefeld University, Bielefeld D-33615, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ralf","family":"M\u00f6ller","sequence":"additional","affiliation":[{"name":"Computer Engineering Group, Faculty of Technology, Bielefeld University, Bielefeld D-33615, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2017,4,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1023\/A:1008821210922","article-title":"Learning View Graphs for Robot Navigation","volume":"5","author":"Franz","year":"1998","journal-title":"Auton. 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