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Learning Traversability Models for Autonomous Mobile Vehicles



Michael O. Shneier, Tommy Chang, Tsai Hong Hong, William P. Shackleford


Autonomous mobile robots need to adapt their behavior to the terrain over which they drive, and to predict the traversability of the terrain so that they can effectively plan their paths. Such robots usually make use of a set of sensors to investigate the terrain around them and build up an internal representation that enable them to navigate. This paper addresses the question of how to use sensor data to learn properties of the environment and use this knowledge to predict which regions of the environment are traversable. The approach makes use of sensed information from range sensors (stereo or ladar), color cameras, and the vehicle?s navigation sensors. Models of terrain regions are learned from subsets of pixels that are selected by projection into a local occupancy grid. The models include color and texture and traversability information obtained from an analysis of the range data associated with the pixels. The models are learned entirely without supervision, deriving their properties from the geometry and the appearance of the scene. The models are used to classify color images and assign traversability costs to regions. The classification does not use the range or position information, but only color images. Traversability determined during the model-building phase is stored in the models. This enables classification of regions beyond the range of stereo or ladar using the information in the color images. The paper describes how the models are constructed and maintained, how they are used to classify image regions, and how the system adapts to changing environments.
Autonomous Robots


classification, color models, learning, Mobility, range, Robotics & Intelligent Systems, texture, traversability, Unmanned Systems, Vision


Shneier, M. , Chang, T. , , T. and Shackleford, W. (2007), Learning Traversability Models for Autonomous Mobile Vehicles, Autonomous Robots, [online], (Accessed June 13, 2024)


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Created November 21, 2007, Updated February 19, 2017