An Investigation Into Trajectory Estimation In Underground Mining Environments Using A Time-Of-Flight Camera And An Inertial Measurement Unit

Thikhathali Terence Ratshidaho, Jules Raymond Tapamo, Jonathan Claassens, Natasha Govender

Abstract


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 robots 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.


Keywords


Time of Flight Camera; ToF; Inertial Measurement Unit; IMU; Kalman filter; Iterative closest point; ICP

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DOI: https://doi.org/10.7166/25-1-736

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Copyright (c) 2015 The South African Journal of Industrial Engineering


ISSN 2224-7890 (on-line) ; ISSN 1012-277X (print)


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