Real-time and Robust Odometry Estimation Using Depth Camera for Indoor Micro Aerial Vehicle
Real-time, robust and precise estimation of a robots ego-motion is a crucial requirement for higher level tasks like autonomous navigation. In this paper, a real-time and robust odometry estimation system for indoor micro aerial vehicle (MAV) is developed by only using the point cloud generated from the depth camera. First, local surface normal features are used to select points with most constraints. Then, an improved iterative closest point method is used to calculate the relative transformation, which is robust against sensor noise and outliers. To further improve the robustness of the estimation, this paper constructs a local point cloud map and compares current point cloud to the local map. Besides, ground plane is also used to simplify the 6DOF estimation problem as a 3DOF estimation problem, which not only reduces the drift but also improve the estimation speed. To validate the performance of the proposed method, we compared our method to several visual odometry methods using different kind of real dataset. The experiment results show that depth only odometry can achieve similar estimation results as state of the art visual odometry methods.
Depth Camera Point Cloud Odometry Estimation Sparse ICP Surface Normal Plane Detection
Zheng Fang Lei Zhang
State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819, China
国际会议
长沙
英文
5254-5259
2014-05-31（万方平台首次上网日期，不代表论文的发表时间）