Multi-Echo LiDAR for 3D Object Detection

TL;DR

This paper introduces a novel 3D object detection model that leverages multiple measurement signals from LiDAR, including echoes, reflectance, and ambient light, to improve detection accuracy over traditional single-echo methods.

Contribution

The paper presents a multi-signal fusion and multi-echo aggregation framework that utilizes full spectrum LiDAR data for enhanced 3D object detection.

Findings

Outperforms state-of-the-art by up to 9.1% in detection accuracy.

Effectively combines multi-echo, reflectance, and ambient signals.

Achieves richer contextual understanding of objects.

Abstract

LiDAR sensors can be used to obtain a wide range of measurement signals other than a simple 3D point cloud, and those signals can be leveraged to improve perception tasks like 3D object detection. A single laser pulse can be partially reflected by multiple objects along its path, resulting in multiple measurements called echoes. Multi-echo measurement can provide information about object contours and semi-transparent surfaces which can be used to better identify and locate objects. LiDAR can also measure surface reflectance (intensity of laser pulse return), as well as ambient light of the scene (sunlight reflected by objects). These signals are already available in commercial LiDAR devices but have not been used in most LiDAR-based detection models. We present a 3D object detection model which leverages the full spectrum of measurement signals provided by LiDAR. First, we propose a multi-signal fusion (MSF) module to combine (1) the reflectance and ambient features extracted with a 2D CNN, and (2) point cloud features extracted using a 3D graph neural network (GNN). Second, we propose a multi-echo aggregation (MEA) module to combine the information encoded in different set of echo points. Compared with traditional single echo point cloud methods, our proposed Multi-Signal LiDAR Detector (MSLiD) extracts richer context information from a wider range of sensing measurements and achieves more accurate 3D object detection. Experiments show that by incorporating the multi-modality of LiDAR, our method outperforms the state-of-the-art by up to 9.1%.