ICRA 2012 Paper Abstract


Paper TuA110.4

McManus, Colin (University of Toronto), Furgale, Paul Timothy (Eidgenössische Technische Hochschule Zürich), Stenning, Braden (University of Toronto), Barfoot, Timothy (University of Toronto)

Visual Teach and Repeat Using Appearance-Based Lidar

Scheduled for presentation during the Interactive Session "Interactive Session TuA-1" (TuA110), Tuesday, May 15, 2012, 08:30−09:00, Ballroom D

2012 IEEE International Conference on Robotics and Automation, May 14-18, 2012, RiverCentre, Saint Paul, Minnesota, USA

This information is tentative and subject to change. Compiled on October 15, 2018

Keywords Autonomous Navigation, Visual Navigation, Field Robots


Visual Teach and Repeat (VT&R) has proven to be an effective method to allow a vehicle to autonomously repeat any previously driven route without the need for a global positioning system. One of the major challenges for a method that relies on visual input to recognize previously visited places is lighting change, as this can make the appearance of a scene look drastically different. For this reason, passive sensors, such as cameras, are not ideal for outdoor environments with inconsistent/inadequate light. However, camera-based systems have been very successful for localization and mapping in outdoor, unstructured terrain, which can be largely attributed to the use of sparse, appearance-based computer vision techniques. Thus, in an effort to achieve lighting invariance and to continue to exploit the heritage of the appearance-based vision techniques traditionally used with cameras, this paper presents the first VT&R system that uses appearance-based techniques with laser scanners for motion estimation. The system has been field tested in a planetary analogue environment for an entire diurnal cycle, covering more than 11km with an autonomy rate of 99.7% of the distance traveled.



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