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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

Abstract

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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