Using prioritized relaxations to locate objects in points clouds for manipulation
This paper considers the problem of identifying objects of interest in laser range point clouds for the purposes of manipulation. One of the characteristics of perception for manipulation is that while it is unnecessary to label all objects in the scene, it may be very important to maximize the likelihood of correctly locating a desired object. This paper leverages this and proposes an approach for locating the most likely object configurations given an object parameterization and a point cloud. While many other approaches to object localization need to explicitly associate points with hypothesized objects, our proposed method avoids this by optimizing relaxations of the likelihood function rather than the exact likelihood. The result is a simple, efficient, and robust method for locating objects that makes few assumptions beyond the desired object parameterization and with few parameters that require tuning.
Robert Truax Robert Platt John Leonard
Computer Science and Artificial Intelligence Laboratory at MIT
国际会议
2011 IEEE International Conference on Robotics and Automation(2011年IEEE世界机器人与自动化大会 ICRA 2011)
上海
英文
2091-2097
2011-05-09（万方平台首次上网日期，不代表论文的发表时间）