ICRA 2011 Paper Abstract


Paper ThP212.3

Achar, Supreeth (Carnegie Mellon University), Sankaran, Bharath (University of Pennsylvania), Nuske, Stephen (CMU Robotics Institute), Scherer, Sebastian (Carnegie Mellon University), Singh, Sanjiv (Carnegie Mellon University)

Self-Supervised Segmentation of River Scenes

Scheduled for presentation during the Regular Sessions "Learning and Adaptive Systems IV" (ThP212), Thursday, May 12, 2011, 15:55−16:10, Room 5H

2011 IEEE International Conference on Robotics and Automation, May 9-13, 2011, Shanghai International Conference Center, Shanghai, China

This information is tentative and subject to change. Compiled on August 18, 2019

Keywords Visual Learning, Visual Navigation, Field Robots


Here we consider the problem of automatically segmenting images taken from a boat or low-flying aircraft. Such a capability is important for autonomous river following and mapping. The need for accurate segmentation in a wide variety of riverine environments challenges the state of the art vision-based methods that have been used in more structured environments such as roads and highways. Apart from the lack of structure, the principal difficulty is the large spatial and temporal variations in the appearance of water in the presence of nearby vegetation and with reflections from the sky. We propose a self-supervised method to segment images into `sky', `river' and `shore' (vegetation + structures) regions. Our approach uses assumptions about river scene structure to learn appearance models based on features like color, texture and image location which are used to segment the image. We validated our algorithm by testing on four datasets captured under varying conditions on different rivers. Our self-supervised algorithm had higher accuracy rates than a supervised alternative, often significantly more accurate, and does not need to be retrained to work under different conditions.



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