Real-time Dynamic Object Detection for Autonomous Driving using Prior 3D-Maps

TL;DR

This paper presents a real-time dynamic object detection method for autonomous driving that leverages prior 3D maps to improve efficiency by formulating detection as background subtraction, evaluated in simulation.

Contribution

The paper introduces a novel background subtraction approach using prior 3D maps for real-time dynamic object detection in autonomous driving.

Findings

Achieved accurate detection in CARLA simulator

Reduced processing by leveraging prior 3D maps

Proposed rejection cascade architecture improves efficiency

Abstract

Lidar has become an essential sensor for autonomous driving as it provides reliable depth estimation. Lidar is also the primary sensor used in building 3D maps which can be used even in the case of low-cost systems which do not use Lidar. Computation on Lidar point clouds is intensive as it requires processing of millions of points per second. Additionally there are many subsequent tasks such as clustering, detection, tracking and classification which makes real-time execution challenging. In this paper, we discuss real-time dynamic object detection algorithms which leverages previously mapped Lidar point clouds to reduce processing. The prior 3D maps provide a static background model and we formulate dynamic object detection as a background subtraction problem. Computation and modeling challenges in the mapping and online execution pipeline are described. We propose a rejection cascade architecture to subtract road regions and other 3D regions separately. We implemented an initial version of our proposed algorithm and evaluated the accuracy on CARLA simulator.