Next Article in Journal
Binaural Range Finding from Synthetic Aperture Computation as the Head is Turned
Previous Article in Journal
Robot-Assisted Crowd Evacuation under Emergency Situations: A Survey
Open AccessArticle

Visual Place Recognition for Autonomous Mobile Robots

Computer Engineering Group, Faculty of Technology, Bielefeld University, Bielefeld D-33615, Germany
Author to whom correspondence should be addressed.
Academic Editor: Huosheng Hu
Robotics 2017, 6(2), 9;
Received: 14 March 2017 / Revised: 10 April 2017 / Accepted: 12 April 2017 / Published: 17 April 2017
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. View Full-Text
Keywords: visual place recognition; holistic image processing; visual compass; robot localization visual place recognition; holistic image processing; visual compass; robot localization
Show Figures

Figure 1

MDPI and ACS Style

Horst, M.; Möller, R. Visual Place Recognition for Autonomous Mobile Robots. Robotics 2017, 6, 9.

AMA Style

Horst M, Möller R. Visual Place Recognition for Autonomous Mobile Robots. Robotics. 2017; 6(2):9.

Chicago/Turabian Style

Horst, Michael; Möller, Ralf. 2017. "Visual Place Recognition for Autonomous Mobile Robots" Robotics 6, no. 2: 9.

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Search more from Scilit
Back to TopTop