题目：Intelligent Robot Localization using Unconventional Sensors
报告人： Simon X. Yang教授，加拿大Guelph大学
时间：2014.12.11, 13:30
地点：信息工程学院（新校）信息楼329
Simon X. Yang(杨先一)，本科毕业于北京大学、硕士毕业于美国休斯顿大学、博士毕业于加拿大阿尔伯塔大学(University of Alberta)。现为加拿大Guelph大学高级机器人与智能系统实验室主任，终身教授，博士生导师。
主要研究领域：移动机器人路径规划与控制、多传感器信息融合、无线传感器网络、智能计算与优化等。国际杂志《IEEE Transactions On Neural Networks》、《IEEE Transactions On Cybernetics》、《International Journal of Robotics and Automation》、《Control and Intelligent Systems》副主编; 国际杂志《International Journal of Computational Intelligence and Applications》、《International Journal of Automation and Systems Engineering》、《Journal of Robotics》、《International Journal of Computing and Information Technology》、《International Journal of Information Acquisition》编委.
报告内容简介：
Localization without prior knowledge of the environment is considered to be one of the most challenging problems in the field of robotics. This talk explores the problem of Simultaneous Localization and Mapping (SLAM) with a focus on combining different sensors to build a more robust, accurate, and reliable localization framework. A high level sensor fusion solution is developed, which enables simple integration of unconventional sensors not typically used for robot localization. A capacitive sensor for sensing floor joists directly under the robot is proposed as an example of an unconventional sensor. A neural network is implemented to combine multiple measurements and build a model of likely joist locations in unexplored regions. Two different sensor fusion approaches are demonstrated. The first solution explores robot localization with a-priori map knowledge. Prior map knowledge removes the requirement for map learning and focuses the problem on fusion of the different sensor maps. With this focus high-level scalable sensor fusion architecture is implemented. Results show an improvement when using this algorithm to incorporate new sensors into the robot localization configuration. The approach also solves the solution where the map is known but the starting location is not. The second fusion approach develops a complete multi-SLAM solution by removing the requirement of a-priori map knowledge. The capacitive sensor is incorporated into the algorithm to demonstrate the scalability of the approach. After incorporating multiple sensors into the solution the peak error and average error of the estimated robot position are both reduced; while simultaneously enabling greater robustness through redundant sensors.
