XDvision: Dense outdoor perception for autonomous vehicles

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XDvision: Dense outdoor perception for autonomous vehicles

Romero-Cano, Victor / Vignard, Nicolas / Laugier, Christian

Robust perception is the cornerstone of safe and environmentally-aware autonomous navigation systems. Autonomous robots are expected to recognise the objects in their surroundings under a wide range of challenging environmental conditions. This problem has been tackled by combining multiple sensor modalities that have complementary characteristics. This paper proposes an approach to multi-sensor-based robotic perception that leverages the rich and dense appearance information provided by camera sensors, and the range data provided by active sensors independently of how dense their measurements are. We introduce a framework we call XDvision where colour images are augmented with dense depth information obtained from sparser sensors such as lidars. We demonstrate the utility of our framework by comparing the performance of a standard CNN-based image classifier fed with image data only with the performance of a two-layer multimodal CNN trained using our augmented representation.