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.
['Colin McManus', 'Paul Timothy Furgale', 'Braden Stenning', 'Timothy D. Barfoot']
Visual Teach and Repeat using appearance-based lidar