SVM-based sensor fusion for improved terrain classification
Department of Electrical and Computer Engineering
Terrain sensing is an important aspect of navigation for autonomous ground vehicles (AGVs) in off-road conditions. Modern AGVs have several sensors that can be used to detect terrain. In this paper, we have implemented terrain classification using a fusion of visual data from a camera and vibrational data from an inertial measurement unit (IMU). The popular supervised learning technique, support vector machine (SVM), has been used due to its high accuracy and relatively small execution time. An image is first captured and the robot then traverses over the region defined by the image to record vibration data. Linear acceleration vectors, perpendicular to the terrain, are extracted from the IMU and statistical features are calculated to make up the vibration data. The images are manually labelled and aligned with the vibration data to create a fused feature vector and train the SVM. Our method has been tested on previously unseen field data and an average accuracy of 90% has been achieved.
Proceedings of SPIE - The International Society for Optical Engineering
SVM-based sensor fusion for improved terrain classification.
Proceedings of SPIE - The International Society for Optical Engineering,
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