Road Information Recognition Based on Multi-Sensor Fusion in Off-Road Environment

In order to achieve road information recognition in off-road environment accurately, a road information recognition method was proposed based on multi-sensor information fusion. Firstly, according to the vibration acceleration signal under vehicle reed, a feature extraction algorithm was designed for road terrain. Integrating the acceleration features and image+depth features based on bilinear pooling method, the method was arranged to realize multi-dimensional feature fusion and recognition of road terrain. Then, in order to improve the detection accuracy of road passable area in off-road environment, a transfer learning method was introduced to transfer the common knowledge of road feature extraction from the off-road road terrain recognition model to the road passable area segmentation model, and trained and tested with a real datum set of off-road terrain. Test results show that the proposed method can not only achieve an average classification accuracy of 98.65% in the task of off-road terrain recognition, but also the introduction of prior knowledge can obviously improve the detection effect of road passable area.