Context-aware video compression for mobile robots

Daniel A. Lazewatsky, Bogumil Giertler, Martha Witick, Leah Perlmutter, Bruce A. Maxwell, William D. Smart

Abstract: Operating robots across networks with unknown, bandwidth, latency and other conditions presents difficulty when the operation depends on real-time feedback and control. Standard video compression methods do a good job compressing arbitrary video, but do not take domain knowledge into account when more information about the video is known beforehand. We have incorporated robot odometry into the video pipeline, allowing video quality to be selectively reduced at times when odometry suggests that such a reduction will not adversely affect task performance of human operators. We found that selectively reducing video quality significantly reduced bandwidth usage, increasing the robot's responsiveness and controllability, while having no measurable effect on task performance.