Optical sensing for robot perception and localization

Abstract

Optical sensing, e.g., computer vision, provides a very compelling approach to solving a number of technological challenges for developing affordable, useful, and reliable robotic products. We describe key advancements in the field consisting of three core technologies for visual pattern recognition (ViPR), visual simultaneous localization and mapping (vSLAM), and a low-cost solution for localization using optical beacons (NorthStar). ViPR is an algorithm for visual pattern recognition based on scale invariant features (SIFT features) which provides a robust and computationally effective solution to fundamental vision problems including the correspondence problem; object recognition; structure; and pose estimation. vSLAM is an algorithm for visual simultaneous localization and mapping using one camera sensor in conjunction with dead-reckoning information, e.g., odometry. vSLAM provides a cost-effective solution to localization and mapping for cluttered environments and is reliable to dynamic changes in the environment Finally, NorthStar uses IR projections onto a surface to estimate the robot's pose based on triangulation. We give examples of concept prototypes as well as commercial products such as Sony's Aibo, which have incorporated these technologies in order to improve product utility and value.