Autonomous Multi-Floor Indoor Navigation with a Computationally Constrained Micro Aerial Vehicle
We are interested in the problem of surveilling and exploring environments that include both indoor and outdoor settings. Aerial vehicles offer mobility and perspective advantages over ground platforms and micro aerial vehicles (MAVs) are particularly applicable to buildings with multiple floors where stairwells can be an obstacle to ground vehicles. A challenge when operating in indoor environments is the lack of an external source of localization such as GPS. For these reasons, in this work we focus on autonomous navigation in buildings with multiple floors without requiring an external source of localization or prior knowledge of the environment. To ensure that the robot is fully autonomous, we require all computation to occur on the robot without need for external infrastructure, communication, or human interaction beyond high-level commands.
Shaojie Shen Nathan Michael Vijay Kumar
GRASP Laboratory,University of Pennsylvania,Philadelphia,PA 19104,USA
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
2968-2969
2011-05-09（万方平台首次上网日期，不代表论文的发表时间）