Efficient and Robust LiDAR-Based End-to-End Navigation

Understand

Deep learning has been used to demonstrate end-to-end neural network learning for autonomous vehicle control from raw sensory input.

• While LiDAR sensors provide reliably accurate information, existing end-to-end driving solutions are mainly based on cameras since processing 3D data requires a large memory footprint and computation cost.
• On the other hand, increasing the robustness of these systems is also critical; however, even estimating the model's uncertainty is very challenging due to the cost of sampling-based methods.
• In this paper, we present an efficient and robust LiDAR-based end-to-end navigation framework.