Fusion Drive: End-to-End Multi Modal Sensor Fusion for Guided Low-Cost Autonomous Vehicle

Abstract

In this paper, we present a supervised learning-based mixed-input sensor fusion neural network for autonomous navigation on a designed track referred to as Fusion Drive. The proposed method combines RGB image and LiDAR laser sensor data for guided navigation along the track and avoidance of learned as well as previously unobserved obstacles for a low-cost embedded navigation system. The proposed network combines separate CNN-based sensor processing into a fully combined network that learns throttle and steering angle labels end-to-end. The proposed network outputs navigational commands with similar learned behavior from the human demonstrations. Performed experiments with validation data-set and in real environment exhibit desired behavior. Recorded performance shows improvement over similar approaches.