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LiDAR for Autonomous Vehicles

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: closed (30 May 2020) | Viewed by 33987

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Special Issue Editors

Prof. Michele Grassi

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Guest Editor

University of Naples Federico II | UNINA · Department of Industrial Engineering
Interests: GNC of space systems; spacecraft relative navigation; pose determination; electro-optical sensors; LIDAR; star tracker; GNSS

Dr. Roberto Opromolla

E-Mail Website
Guest Editor

Department of Industrial Engineering, University of Naples Federico II, Piazzale Tecchio 80, 80125 Naples, Italy
Interests: spacecraft guidance; navigation and control; spacecraft relative navigation; pose determination; electro-optical sensors; LIDAR; star tracker; unmanned aerial vehicles; autonomous navigation; sense and avoid; visual detection and tracking
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Special Issue Information

Dear Colleagues,

LiDAR systems represent a key technology to enhance the capabilities of future autonomous vehicles in terms of navigation and situational awareness. Indeed, significant advancements are needed by terrestrial, marine, aerial, and space vehicles to meet the levels of safety and performance required within present and future application scenarios.
Self-driving cars, boats, and drones are expected to rely on LIDAR to enable autonomous and safe navigation, especially in GNSS-denied (e.g., indoor, underground, underwater) and GNSS-challenging (e.g., natural and urban canyon) areas. This task requires the development of accurate and robust localization algorithms, e.g., based on the concepts of odometry, and simultaneous localization and mapping. These approaches will exploit either LIDAR alone, or the integration of multiple sensors (e.g., inertial, cameras, radars), taking advantage of sensor fusion or cross-sensor cueing strategies. Regarding situational awareness, LIDAR can be used on board autonomous vehicle for detection and avoidance of static and moving obstacles, and for monitoring/inspecting infrastructures like roads, pipelines, and powerlines. These systems can also assist precision agriculture applications (especially on board autonomous aerial vehicles) by mapping water flow and catchments, monitoring erosion and soil loss, providing 3D models of crops, forestry, and vegetation.
LIDARs play a key role also in the space domain. On one side, mission scenarios such as on-orbit servicing and active debris removal, which require an autonomous spacecraft (chaser) to safely perform maneuvers (such as rendezvous and docking) in close proximity with respect to uncooperative space targets, can rely on LIDAR to provide accurate estimates of the target-chaser relative state. This task requires advanced pose determination algorithms as well as robust relative navigation architectures. On the other side, LIDAR-based navigation systems are important for future deep space exploration scenarios, e.g., during precise descent and landing operations on planets, comets or asteroids. In this framework, advanced algorithmic solutions shall be developed for both navigation, and hazard detection and avoidance (landing site selection).

Hence, this Special Issue welcomes original research contributions and state-of-the-art reviews, from academia and industry, regarding the use of LIDAR technologies on board autonomous vehicles. In addition to state-of-the-art LIDARs (e.g., scanning and flash LIDAR), innovative solutions based on the use of solid-state technologies are also welcome. The Special Issue topics include but are not limited to:

Autonomous navigation solutions (e.g., odometry, simultaneous localization, and mapping);
Autonomous detect and avoid (e.g., for static or moving obstacles);
Infrastructure (e.g., powerlines, pipelines, roads) inspection and monitoring;
3D mapping;
Precision agriculture (e.g., canopy height estimation, erosion, and soil loss monitoring);
Spacecraft relative navigation and pose determination;
Autonomous navigation and situational awareness in deep space exploration scenarios (e.g., hazard detection and precise landing).

Prof. Michele Grassi
Prof. Roberto Opromolla
Guest Editors

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Keywords

Scanning, flash and solid-state LIDARs for autonomous vehicles
Unmanned ground vehicles (UGV)
Unmanned surface vehicles (USV)
Unmanned aerial vehicles (UAV)
Localization by odometry
Localization by simultaneous localization and mapping
Obstacle detection and avoidance
Infrastructure inspection and monitoring
3D mapping
Precision agriculture
Spacecraft relative navigation and pose determination
Planetary landing
Hazard detection

Published Papers (7 papers)

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Research

17 pages, 8157 KiB

Open AccessArticle

How Accurate Can UWB and Dead Reckoning Positioning Systems Be? Comparison to SLAM Using the RPLidar System

by Damian Grzechca, Adam Ziębiński, Krzysztof Paszek, Krzysztof Hanzel, Adam Giel, Marcin Czerny and Andreas Becker

Sensors 2020, 20(13), 3761; https://0-doi-org.brum.beds.ac.uk/10.3390/s20133761 - 05 Jul 2020

Cited by 9 | Viewed by 4103

Abstract

This paper compares two positioning systems, namely ultra-wideband (UWB) based micro-location technology and dead reckoning and a RPLidar based simultaneous localization and mapping (SLAM) solution. This new approach can be used to improve the quality of the positioning system and increase the functionality of advanced driver assistance systems (ADAS). This is achieved by using stationary nodes and UWB tags on the vehicles. Thus, the redundancy of localization can be achieved by this approach, e.g., as a backup to onboard sensors like RPlidar or radar. Additionally, UWB based micro-location allows additional data channels to be used for communication purposes. Furthermore, it is shown that the regular use of correction data increases UWB and dead reckoning accuracy. These correction data can be based on onboard sensors. This shows that it is promising to develop a system that fuses onboard sensors and micro-localization for safety-critical tasks like the platooning of commercial vehicles. Full article

(This article belongs to the Special Issue LiDAR for Autonomous Vehicles)

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26 pages, 51114 KiB

Open AccessArticle

Reshaping Field of View and Resolution with Segmented Reflectors: Bridging the Gap between Rotating and Solid-State LiDARs

by Atle Aalerud, Joacim Dybedal and Dipendra Subedi

Sensors 2020, 20(12), 3388; https://0-doi-org.brum.beds.ac.uk/10.3390/s20123388 - 15 Jun 2020

Cited by 8 | Viewed by 4386

Abstract

This paper describes the first simulations and experimental results of a novel segmented Light Detection And Ranging (LiDAR) reflector. Large portions of the rotating LiDAR data are typically discarded due to occlusion or a misplaced field of view (FOV). The proposed reflector solves this problem by reflecting the entire FOV of the rotating LiDAR towards a target. Optical simulation results, using Zemax OpticStudio, suggest that adding a reflector reduces the range of the embedded LiDAR with only

3.9

%. Furthermore, pattern simulation results show that a radially reshaped FOV can be configured to maximize point cloud density, maximize coverage, or a combination. Here, the maximum density is defined by the number of mirror segments in the reflector. Finally, a prototype was used for validation. Intensity, Euclidean error, and sample standard deviation were evaluated and, except for reduced-intensity values, no significant reduction in the LiDAR’s performance was found. Conversely, the number of usable measurements increased drastically. The mirrors of the reflector give the LiDAR multiple viewpoints to the target. Ultimately, it is argued that this can enhance the object revisit rate, instantaneous resolution, object classification range, and robustness against occlusion and adverse weather conditions. Consequently, the reflector design enables long-range rotating LiDARs to achieve the robust super-resolution needed for autonomous driving at highway speeds. Full article

(This article belongs to the Special Issue LiDAR for Autonomous Vehicles)

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Graphical abstract

13 pages, 1021 KiB

Open AccessArticle

Color Image Generation from LiDAR Reflection Data by Using Selected Connection UNET

by Hyun-Koo Kim, Kook-Yeol Yoo and Ho-Youl Jung

Sensors 2020, 20(12), 3387; https://0-doi-org.brum.beds.ac.uk/10.3390/s20123387 - 15 Jun 2020

Cited by 6 | Viewed by 4263

Abstract

In this paper, a modified encoder-decoder structured fully convolutional network (ED-FCN) is proposed to generate the camera-like color image from the light detection and ranging (LiDAR) reflection image. Previously, we showed the possibility to generate a color image from a heterogeneous source using the asymmetric ED-FCN. In addition, modified ED-FCNs, i.e., UNET and selected connection UNET (SC-UNET), have been successfully applied to the biomedical image segmentation and concealed-object detection for military purposes, respectively. In this paper, we apply the SC-UNET to generate a color image from a heterogeneous image. Various connections between encoder and decoder are analyzed. The LiDAR reflection image has only 5.28% valid values, i.e., its data are extremely sparse. The severe sparseness of the reflection image limits the generation performance when the UNET is applied directly to this heterogeneous image generation. In this paper, we present a methodology of network connection in SC-UNET that considers the sparseness of each level in the encoder network and the similarity between the same levels of encoder and decoder networks. The simulation results show that the proposed SC-UNET with the connection between encoder and decoder at two lowest levels yields improvements of 3.87 dB and 0.17 in peak signal-to-noise ratio and structural similarity, respectively, over the conventional asymmetric ED-FCN. The methodology presented in this paper would be a powerful tool for generating data from heterogeneous sources. Full article

(This article belongs to the Special Issue LiDAR for Autonomous Vehicles)

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25 pages, 16120 KiB

Open AccessArticle

Automotive Lidar Modelling Approach Based on Material Properties and Lidar Capabilities

by Stefan Muckenhuber, Hannes Holzer and Zrinka Bockaj

Sensors 2020, 20(11), 3309; https://0-doi-org.brum.beds.ac.uk/10.3390/s20113309 - 10 Jun 2020

Cited by 22 | Viewed by 7615

Abstract

Development and validation of reliable environment perception systems for automated driving functions requires the extension of conventional physical test drives with simulations in virtual test environments. In such a virtual test environment, a perception sensor is replaced by a sensor model. A major challenge for state-of-the-art sensor models is to represent the large variety of material properties of the surrounding objects in a realistic manner. Since lidar sensors are considered to play an essential role for upcoming automated vehicles, this paper presents a new lidar modelling approach that takes material properties and corresponding lidar capabilities into account. The considered material property is the incidence angle dependent reflectance of the illuminated material in the infrared spectrum and the considered lidar property its capability to detect a material with a certain reflectance up to a certain range. A new material classification for lidar modelling in the automotive context is suggested, distinguishing between 7 material classes and 23 subclasses. To measure angle dependent reflectance in the infrared spectrum, a new measurement device based on a time of flight camera is introduced and calibrated using Lambertian targets with defined reflectance values at

10 %

50 %

, and

95 %

. Reflectance measurements of 9 material subclasses are presented and 488 spectra from the NASA ECOSTRESS library are considered to evaluate the new measurement device. The parametrisation of the lidar capabilities is illustrated by presenting a lidar measurement campaign with a new Infineon lidar prototype and relevant data from 12 common lidar types. Full article

(This article belongs to the Special Issue LiDAR for Autonomous Vehicles)

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20 pages, 10266 KiB

Open AccessArticle

A Fast Spatial Clustering Method for Sparse LiDAR Point Clouds Using GPU Programming

by Yifei Tian, Wei Song, Long Chen, Yunsick Sung, Jeonghoon Kwak and Su Sun

Sensors 2020, 20(8), 2309; https://0-doi-org.brum.beds.ac.uk/10.3390/s20082309 - 18 Apr 2020

Cited by 5 | Viewed by 3701

Abstract

Fast and accurate obstacle detection is essential for accurate perception of mobile vehicles’ environment. Because point clouds sensed by light detection and ranging (LiDAR) sensors are sparse and unstructured, traditional obstacle clustering on raw point clouds are inaccurate and time consuming. Thus, to achieve fast obstacle clustering in an unknown terrain, this paper proposes an elevation-reference connected component labeling (ER-CCL) algorithm using graphic processing unit (GPU) programing. LiDAR points are first projected onto a rasterized x–z plane so that sparse points are mapped into a series of regularly arranged small cells. Based on the height distribution of the LiDAR point, the ground cells are filtered out and a flag map is generated. Next, the ER-CCL algorithm is implemented on the label map generated from the flag map to mark individual clusters with unique labels. Finally, obstacle labeling results are inverse transformed from the x–z plane to 3D points to provide clustering results. For real-time 3D point cloud clustering, ER-CCL is accelerated by running it in parallel with the aid of GPU programming technology. Full article

(This article belongs to the Special Issue LiDAR for Autonomous Vehicles)

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22 pages, 1668 KiB

Open AccessArticle

Extrinsic Calibration of Multiple Two-Dimensional Laser Rangefinders Based on a Trihedron

by Fei Zhu, Yuchun Huang, Zizhu Tian and Yaowei Ma

Sensors 2020, 20(7), 1837; https://0-doi-org.brum.beds.ac.uk/10.3390/s20071837 - 26 Mar 2020

Cited by 16 | Viewed by 2957

Abstract

Multiple two-dimensional laser rangefinders (LRFs) are applied in many applications like mobile robotics, autonomous vehicles, and three-dimensional reconstruction. The extrinsic calibration between LRFs is the first step to perform data fusion and practical application. In this paper, we proposed a simple method to calibrate LRFs based on a corner composed of three mutually perpendicular planes. In contrast to other methods that require a special pattern or assistance from other sensors, the trihedron corner needed in this method is common in daily environments. In practice, we can adjust the position of the LRFs to observe the corner until the laser scanning plane intersects with three planes of the corner. Then, we formed a Perspective-Three-Point problem to solve the position and orientation of each LRF at the common corner coordinate system. The method was validated with synthetic and real experiments, showing better performance than existing methods. Full article

(This article belongs to the Special Issue LiDAR for Autonomous Vehicles)

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19 pages, 10139 KiB

Open AccessArticle

Optimized LOAM Using Ground Plane Constraints and SegMatch-Based Loop Detection

by Xiao Liu, Lei Zhang, Shengran Qin, Daji Tian, Shihan Ouyang and Chu Chen

Sensors 2019, 19(24), 5419; https://0-doi-org.brum.beds.ac.uk/10.3390/s19245419 - 09 Dec 2019

Cited by 23 | Viewed by 4795

Abstract

Reducing the cumulative error in the process of simultaneous localization and mapping (SLAM) has always been a hot issue. In this paper, in order to improve the localization and mapping accuracy of ground vehicles, we proposed a novel optimized lidar odometry and mapping method using ground plane constraints and SegMatch-based loop detection. We only used the lidar point cloud to estimate the pose between consecutive frames, without any other sensors, such as Global Positioning System (GPS) and Inertial Measurement Unit (IMU). Firstly, the ground plane constraints were used to reduce matching errors. Then, based on more accurate lidar odometry obtained from lidar odometry and mapping (LOAM), SegMatch completed segmentation matching and loop detection to optimize the global pose. The neighborhood search was also used to accomplish the loop detection task in case of failure. Finally, the proposed method was evaluated and compared with the existing 3D lidar SLAM methods. Experiment results showed that the proposed method could realize low drift localization and dense 3D point cloud map construction. Full article

(This article belongs to the Special Issue LiDAR for Autonomous Vehicles)

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