SVM-based sensor fusion for improved terrain classification

Akhil Kurup; Sam Kysar; Jeremy Bos

doi:10.1117/12.2558960

13 May 2020 SVM-based sensor fusion for improved terrain classification

Akhil Kurup, Sam Kysar, Jeremy Bos

Proceedings Volume 11415, Autonomous Systems: Sensors, Processing, and Security for Vehicles and Infrastructure 2020; 114150G (2020) https://doi.org/10.1117/12.2558960
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

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.

Conference Presentation

Citation Download Citation

Akhil Kurup, Sam Kysar, and Jeremy Bos "SVM-based sensor fusion for improved terrain classification", Proc. SPIE 11415, Autonomous Systems: Sensors, Processing, and Security for Vehicles and Infrastructure 2020, 114150G (13 May 2020); https://doi.org/10.1117/12.2558960

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