3 accepted papers
Assessing terrain ahead of a robot when repeating previously driven safe paths can be accomplished by looking for geometric changes (e.g., due to the appearance of humans or other obstacles). Previous work has shown that the incorporation of data-driven learning and place-dependence are useful aspec…
This paper presents a learned, place-dependent terrain-assessment classifier that improves over time. Whereas typical methods aim to assess all of the terrain in a given environment, we exploit the fact that many robotic navigation tasks are well-suited to visual-teach-and-repeat navigation where ro…
This paper presents an approach to learning robot terrain assessment from human demonstration. An operator drives a robot for a short period of time, supervising the gathering of traversable and untraversable terrain data. After this initial training period, the robot can then predict the traversabi…