Road slope real-time detection for unmanned truck in surface mine

doi:10.11947/j.AGCS.2021.20210242

Abstract

Abstract: Given the steepness of road slope in surface mines, one unmanned truck may run at risk in unknown environment if it can’t plan a proper speed in advance. Therefore, it is crucial for an autonomous vehicle to perceive an accurate value of road slope of it in real time. However, the accuracy is hard to achieve in existing methods, including global navigation satellite system (GNSS), inertial navigation system (INS) and simultaneous localization and mapping (SLAM). For GNSS or INS, they can measure a truck’s angle of pitch, but this angle cannot be equal to road slope due to its large pitching and bouncing movements caused by steep and uneven roads. For the same reason, SLAM doesn’t work well either. Also, it will lose efficacy as geometric features are not obvious in open-pit mine. To deal with these challenges, this paper proposes a grid Kalman road slope real-time detection (GKSRD) method. The method’s input is 3D point cloud of lidar and the pitch of INS. And the method uses a 2D grid map, an iterative optimization algorithm in rectangular region of interest and the Kalman filter. Compared with methods based on GNSS or INS, this method minimizes the error of slope detection. Different from methods based on SLAM, this method does not rely on geometric features of the environment. Verified by experiments, the average error of the road slope detected by GKSRD method is less than 0.01 degree, and the maximum error is less than 0.5 degree. Hence, compared with methods based on INS or GNSS and methods based on SLAM, GKSRD method is more accurate, stable and adaptive.

Key words: unmanned truck, road slope real-time detection, 3D LiDAR, surface mine

CLC Number:

P227

MENG Dejiang, TIAN Bin, CAI Feng, GAO Yijun, CHEN Long. Road slope real-time detection for unmanned truck in surface mine[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(11): 1628-1638.

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