Abstract
Autonomous mobile robot navigation, either off-road or on ill-structured roads, presents unique challenges for machine perception. A successful terrain or roadway classifier must be able to learn in a self-supervised manner and adapt to inter- and intra-run changes in the local environment.
This paper demonstrates the improvements achieved by augmenting an existing self-supervised image segmentation procedure with an additional supervisory input. Obstacles and roads may differ in appearance at distance because of illumination and texture frequency properties. Reverse optical flow is added as an input to the image segmentation technique to find examples of a region of interest at previous times in the past. This provides representations of this region at multiple scales and allows the robot to better determine where more examples of this class appear in the image.
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Lookingbill, A., Rogers, J., Lieb, D. et al. Reverse Optical Flow for Self-Supervised Adaptive Autonomous Robot Navigation. Int J Comput Vision 74, 287–302 (2007). https://doi.org/10.1007/s11263-006-0024-x
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DOI: https://doi.org/10.1007/s11263-006-0024-x