AN INVESTIGATION INTO TRAJECTORY ESTIMATION IN UNDERGROUND MINING ENVIRONMENTS USING A TIME-OF-FLIGHT CAMERA AND AN INERTIAL MEASUREMENT UNIT

作者：Ratshidaho T T; Tapamo J R^*; Claassens J; Govender N

来源：South African Journal of Industrial Engineering, 2014, 25(1): 145-161.

DOI：10.7166/25-1-736

One of the most important and challenging tasks for mobile robots that navigate autonomously is localisation - the process whereby a robot locates itself within a map of a known environment or with respect to a known starting point within an unknown environment. Localisation of a robot in an unknown environment is done by tracking the trajectory of the robot on the basis of the initial pose. Trajectory estimation becomes a challenge if the robot is operating in an unknown environment that has a scarcity of landmarks, is GPS-denied, has very little or no illumination, and is slippery - such as in underground mines. This paper attempts to solve the problem of estimating a robot's trajectory in underground mining environments using a time-of-flight (ToF) camera and an inertial measurement unit (IMU). In the past, this problem has been addressed by using a 3D laser scanner; but these are expensive and consume a lot of power, even though they have high measurement accuracy and a wide field of view. Here, trajectory estimation is accomplished by the fusion of ego-motion provided by the ToF camera with measurement data provided by a low cost IMU. The fusion is performed using the Kalman filter algorithm on a mobile robot moving on a 2D planar surface. The results show a significant improvement on the trajectory estimation. A Vicon system is used to provide groundtruth for the trajectory estimation. Trajectory estimation only using the ToF camera is prone to errors, especially when the robot is rotating; but the fused trajectory estimation algorithm is able to estimate accurate ego-motion even when the robot is rotating.

• 出版日期2014-5