FuseMODNet: Real-Time Camera and LiDAR based Moving Object Detection for robust low-light Autonomous Driving

TL;DR

This paper introduces FuseMODNet, a real-time CNN-based method that fuses camera and LiDAR data to improve moving object detection in low-light conditions for autonomous driving, demonstrating significant performance gains.

Contribution

The paper presents a novel CNN architecture that combines camera and LiDAR data for robust, real-time moving object detection in low-light environments, including a new dataset and improved accuracy.

Findings

10.1% improvement on Dark KITTI dataset

4.25% improvement on standard KITTI dataset

Runs at 18 fps on a standard GPU

Abstract

Moving object detection is a critical task for autonomous vehicles. As dynamic objects represent higher collision risk than static ones, our own ego-trajectories have to be planned attending to the future states of the moving elements of the scene. Motion can be perceived using temporal information such as optical flow. Conventional optical flow computation is based on camera sensors only, which makes it prone to failure in conditions with low illumination. On the other hand, LiDAR sensors are independent of illumination, as they measure the time-of-flight of their own emitted lasers. In this work, we propose a robust and real-time CNN architecture for Moving Object Detection (MOD) under low-light conditions by capturing motion information from both camera and LiDAR sensors. We demonstrate the impact of our algorithm on KITTI dataset where we simulate a low-light environment creating a novel dataset "Dark KITTI". We obtain a 10.1% relative improvement on Dark-KITTI, and a 4.25% improvement on standard KITTI relative to our baselines. The proposed algorithm runs at 18 fps on a standard desktop GPU using $256\times1224$ resolution images.