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A qualitative study of LiDAR technologies and their application areas

Abstract

Accurate registration of subsea Light Detection and Ranging (LiDAR) point clouds is critical for offshore metrology, where millimeter-level errors can significantly impact operational cost and risk. This study evaluates automated registration methods for static scan positions acquired by Kraken Robotics systems. Two approaches were implemented in Open3D: a hierarchical tree-based Iterative Closest Point (ICP) method and a pose-graph multiway registration framework. The methods were tested on multiple real subsea datasets containing 19–33 high-density scans per subsea scene and on synthetic datasets generated with Digital Imaging and Remote Sensing Image Generation (DIRSIG) to enable ground-truth evaluation. Results show that multiway registration provides improved global consistency, lower adjacent-scan Root Mean Square Error RMSE, and reduced processing time compared to tree-based ICP. Ground-truth analysis demonstrated sub–sampling-level performance, corresponding to an expected 1–4 mm alignment accuracy for real datasets. Global coarse registration provided no measurable benefit for well-initialized static surveys. The final analysis demonstrates that multiway registration enables accurate, efficient, and fully automated subsea LiDAR alignment, reducing manual effort and improving metrology reliability.

Digital Library: EI

Published Online: March 2026

Article

230 75

LiDAR
LiDAR systems
LiDAR applications
LiDAR technology
3D modeling

Daniel Jaster, Reiner Creutzburg, Eberhard Hasche

DOI

10.2352/EI.2023.35.3.MOBMU-368

Volume 35

Issue 3

Abstract

In this work, the most relevant 3D LiDAR technologies and their applications in 2022 were investigated. For this purpose, applications of LiDAR systems were classified into the typical application areas "3D modeling", "smart city", "robotics", "smart automotive" and "consumer goods". The investigation has shown that neither "mechanical" LiDAR technologies, nor so-called solid-state LiDAR technologies, nor "hybrid" LiDAR technologies can be evaluated as optimal for the typical application areas. In none of the application areas could all of the elaborated requirements be met. However, the "hybrid" LiDAR technologies such as sequential MEMS LiDAR technology and sequential flash LiDAR technology proved to be among the most suitable for most typical application areas. However, other technologies also tended to be suitable for individual typical application areas. Finally, it was found that several of the LiDAR technologies investigated are currently equally suitable for some typical application areas. To evaluate the suitability, concrete LiDAR systems - of different technologies and properties - were compared with the specific requirements of exemplary applications of an application area. The results of the investigation provide an orientation as to which LiDAR technology is promising for which application area.

Digital Library: EI

Published Online: January 2023

Data driven degradation of automotive sensors and effect analysis

339 77

sensor
AI
Automotive
Validation
Camera
multimodal
LiDAR
Radar

Sven Fleck, Benjamin May, Gwen Daniel, Chris Davies

Pages 180-1 - 180-8, January 2021, © Society for Imaging Science and Technology 2021

DOI

10.2352/ISSN.2470-1173.2021.17.AVM-180

Volume 33

Issue 17

Autonomous driving plays a crucial role to prevent accidents and modern vehicles are equipped with multimodal sensor systems and AI-driven perception and sensor fusion. These features are however not stable during a vehicle’s lifetime due to various means of degradation. This introduces an inherent, yet unaddressed risk: once vehicles are in the field, their individual exposure to environmental effects lead to unpredictable behavior. The goal of this paper is to raise awareness of automotive sensor degradation. Various effects exist, which in combination may have a severe impact on the AI-based processing and ultimately on the customer domain. Failure mode and effects analysis (FMEA) type approaches are used to structure a complete coverage of relevant automotive degradation effects. Sensors include cameras, RADARs, LiDARs and other modalities, both outside and in-cabin. Sensor robustness alone is a well-known topic which is addressed by DV/PV. However, this is not sufficient and various degradations will be looked at which go significantly beyond currently tested environmental stress scenarios. In addition, the combination of sensor degradation and its impact on AI processing is identified as a validation gap. An outlook to future analysis and ways to detect relevant sensor degradations is also presented.

Digital Library: EI

Published Online: January 2021

LiDAR-Camera Fusion for 3D Object Detection

282 100

Object detection
Sensor Fusion
Robotics
Computer Vision
LiDAR
camera
3d

Darshan Bhanushali, Robert Relyea, Karan Manghi, Abhishek Vashist, Clark Hochgraf, Amlan Ganguly, Andres Kwasinski, Michael E. Kuhl, Raymond Ptucha

DOI

10.2352/ISSN.2470-1173.2020.16.AVM-257

Volume 32

Issue 16

The performance of autonomous agents in both commercial and consumer applications increases along with their situational awareness. Tasks such as obstacle avoidance, agent to agent interaction, and path planning are directly dependent upon their ability to convert sensor readings into scene understanding. Central to this is the ability to detect and recognize objects. Many object detection methodologies operate on a single modality such as vision or LiDAR. Camera-based object detection models benefit from an abundance of feature-rich information for classifying different types of objects. LiDAR-based object detection models use sparse point clouds, where each point contains accurate 3D position of object surfaces. Camera-based methods lack accurate object to lens distance measurements, while LiDAR-based methods lack dense feature-rich details. By utilizing information from both camera and LiDAR sensors, advanced object detection and identification is possible. In this work, we introduce a deep learning framework for fusing these modalities and produce a robust real-time 3D bounding box object detection network. We demonstrate qualitative and quantitative analysis of the proposed fusion model on the popular KITTI dataset.

Digital Library: EI

Published Online: January 2020

Improving Multimodal Localization Through Self-Supervision

192 8

Robotics
Deep Learning
Self-supervision
LiDAR
Sensor Fusion
Localization

Robert Relyea, Darshan Bhanushali, Karan Manghi, Abhishek Vashist, Clark Hochgraf, Amlan Ganguly, Andres Kwasinski, Michael E. Kuhl, Raymond Ptucha

DOI

10.2352/ISSN.2470-1173.2020.6.IRIACV-014

Volume 32

Issue 6

Modern warehouses utilize fleets of robots for inventory management. To ensure efficient and safe operation, real-time localization of each agent is essential. Most robots follow metal tracks buried in the floor and use a grid of precisely mounted RFID tags for localization. As robotic agents in warehouses and manufacturing plants become ubiquitous, it would be advantageous to eliminate the need for these metal wires and RFID tags. Not only do they suffer from significant installation costs, the removal of wires would allow agents to travel to any area inside the building. Sensors including cameras and LiDAR have provided meaningful localization information for many different positioning system implementations. Fusing localization features from multiple sensor sources is a challenging task especially when the target localization task’s dataset is small. We propose a deep-learning based localization system which fuses features from an omnidirectional camera image and a 3D LiDAR point cloud to create a robust robot positioning model. Although the usage of vision and LiDAR eliminate the need for the precisely installed RFID tags, they do require the collection and annotation of ground truth training data. Deep neural networks thrive on lots of supervised data, and the collection of this data can be time consuming. Using a dataset collected in a warehouse environment, we evaluate the performance of two individual sensor models for localization accuracy. To minimize the need for extensive ground truth data collection, we introduce a self-supervised pretraining regimen to populate the image feature extraction network with meaningful weights before training on the target localization task with limited data. In this research, we demonstrate how our self-supervision improves accuracy and convergence of localization models without the need for additional sample annotation.

Digital Library: EI

Published Online: January 2020

Multimodal Localization for Autonomous Agents

103 6

LiDAR
Omnidirectional Camera
Robotics
Localization
Deep Neural Network
Impulse Radio Ultra Wide Band
60 GHz Wireless

Robert Relyea, Darshan Bhanushali, Abhishek Vashist, Amlan Ganguly, Andres Kwasinski, Michael E Kuhl, Raymond Ptucha

DOI

10.2352/ISSN.2470-1173.2019.7.IRIACV-451

Volume 31

Issue 7