Application of Log-Hough Transform to Lidar Navigation

Log-Hough变换在激光雷达导航中的应用

• 批准号: 9361990
• 负责人: Carl F. Weiman
• 金额: $ 6.44万
• 依托单位: Transitions Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1994
• 资助国家: 美国
• 起止时间: 1994-02-15 至 1994-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9361990&HistoricalAwards=false
• 关键词: Application; Log; Hough; Transform; Lidar

9361990 Weiman Efficient sensory perception and processing are crucial to mobile robot navigation. As sensor technology advances, conventional processing methods become a bottleneck, crippling real time operation. Transitions Research Corporation has recently develop a low-cost LIDAR system capable of generating 10,000 samples per second, challenging conventional sensor data processing rates. This research project will efficiently and robustly compress sensory range-bearing data by applying the log-Hough transform to recognize walls and characterize them parametrically. Walls are the dominant navigation reference features in built environments. The log-Hough transform converts a long chain of sensor hits on a wall to a single data point representing line parameters. This succinct data can be used for localization against a priori maps without overloading communications channels to the navigator. In Phase I, LIDAR data will be gathered and analyzed with respect to range and bearing resolution; software will implement the log-Hough transform for these data. In Phase II, the method will be implemented in a real time prototype navigation system running on TRC's HelpMate mobile robot. In Phase III, the method will be incorporated into a commercial product for mobile robots navigation and obstacle avoidance.

高效的感官感知和处理是移动机器人导航的关键。随着传感器技术的进步，传统的处理方法成为一个瓶颈，削弱了实时操作。过渡研究公司最近开发了一种低成本的激光雷达系统，能够每秒产生10,000个样本，挑战传统传感器的数据处理速度。本课题将利用log-Hough变换对感知距离数据进行有效、鲁棒的压缩，对墙体进行识别和参数化表征。墙壁是建筑环境中主要的导航参考特征。log-Hough变换将传感器在墙上的长链命中值转换为表示线参数的单个数据点。这些简洁的数据可以用于针对先验地图的本地化，而无需重载导航器的通信通道。在第一阶段，将收集和分析激光雷达数据的距离和方位分辨率；软件将对这些数据进行log-Hough变换。在第二阶段，该方法将在TRC的HelpMate移动机器人上运行的实时原型导航系统中实施。在第三阶段，该方法将被纳入移动机器人导航和避障的商业产品中。