Performance Analysis of 10 Models of 3D LiDARs for Automated Driving

Abstract

Automated vehicle technology has recently become reliant on 3D LiDAR sensing for perception tasks such as mapping, localization and object detection. This has led to a rapid growth in the LiDAR manufacturing industry with several competing makers releasing new sensors regularly. With this increased variety of LiDARs, each with different properties such as number of laser emitters, resolution, field-of-view, and price tags, a more in-depth comparison of their characteristics and performance is required. This work compares 10 commonly used 3D LiDARs, establishing several metrics to assess their performance. Various outstanding issues with specific LiDARs were qualitatively identified. The accuracy and precision of individual LiDAR beams and accumulated point clouds are evaluated in a controlled environment at distances from 5 to 180 meters. Reflective targets were used to characterize intensity patterns and quantify the impact of surface reflectivity on accuracy and precision. A vehicle and pedestrian mannequin were also used as additional targets of interest. A thorough assessment of these LiDARs is given with their potential applicability for automated driving tasks. The data collected in these experiments and analysis tools are all shared openly.

Direct Time-of-Flight Single-Photon Imaging

“…The longer measurement range of dToF (limited by optical power budget) and array implementation for faster frame rate has led widespread adoption in the burgeoning automotive LiDAR field for autonomous vehicles (AVs) and advanced driver assistance systems (ADAS). SPAD based dToF is now embedded in a variety of automotive LiDAR prototypes with numerous approaches to light projection and scanning [18][19].…”

All-Solid-State Beam Steering via Integrated Optical Phased Array Technology

“…Mechanical LiDAR is one of the most common types of LiDAR, and has the characteristics of remote detection and large FOV [18,19]. However, such types of LiDAR are bulky, power-hungry, and vulnerable to mechanical shock [20].…”

A Deep Learning Approach for LiDAR Resolution-Agnostic Object Detection

“…However, less research has been conducted on domain adaptation or portability on 3D point clouds, especially in an outdoor sensor data. As shown in [39], each 3D LiDAR sensor has its own set of characteristics corresponding to a range, point distribution, data coherency on disturbed conditions,etc. The authors of [40] perform an in-depth analysis of the performance of their architecture for semantic segmentation of point clouds from a 32-channel and a 128-channel LiDAR.…”