Research on the Applicability of Monocular 3d Object Detection Using CARLA Simulator

Abstract

Monocular 3d object detection methods are promising in the field of making autonomous robots without lidar, which can reduce costs of production significantly. However monocular 3d object detection methods tend to have low precision due to inaccurate inference of distances to objects. Nevertheless, there are several ways to measure the impact of detection precision on the downstream autonomous driving task. In this work, autonomous agents which use lidar, monocular camera, and ground truth for 3d object detection are compared in the CARLA simulator. Each agent has passed a set of routes with challenging traffic situations, totaling 122.5 km driven. Quality of movement was assessed using the collisions statistics, as a result, the agent using a monocular camera performed 4.5% better than the agent using lidar. This indicates the applicability of monocular 3d object detection algorithms in certain cases.