An Extensible Multi-Sensor Fusion Framework for 3D Imaging

Abstract

Many autonomous vehicles rely on an array of sensors for safe navigation, where each sensor captures different visual attributes from the surrounding environment. For example, a single conventional camera captures high-resolution images but no 3D information; a LiDAR provides excellent range information but poor spatial resolution; and a prototype single-photon LiDAR (SP-LiDAR) can provide a dense but noisy representation of the 3D scene. Although the outputs of these sensors vary dramatically (e.g., 2D images, point clouds, 3D volumes), they all derive from the same 3D scene. We propose an extensible sensor fusion framework that (1) lifts the sensor output to volumetric representations of the 3D scene, (2) fuses these volumes together, and (3) processes the resulting volume with a deep neural network to generate a depth (or disparity) map. Although our framework can potentially extend to many types of sensors, we focus on fusing combinations of three imaging systems: monocular/stereo cameras, regular LiDARs, and SP-LiDARs. To train our neural network, we generate a synthetic dataset through CARLA that contains the individual measurements. We also conduct various fusion ablation experiments and evaluate the results of different sensor combinations.