Time-of-flight sensor for getting shape model of automobiles toward digital 3D imaging approach of autonomous driving

Niskanen, Ilpo; Immonen, Matti; Hallman, Lauri; Yamamuchi, Genki; Mikkonen, Martti; Hashimoto, Takeshi; Nitta, Yasushi; Keränen, Pekka; Kostamovaara, J.; Heikkilä, Rauno

doi:10.1016/j.autcon.2020.103429

Cited by 11 publications

(7 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although the high manufacturing difficulty of SPAD leads to fewer mature commercial dTOF cameras, the irradiance power of light pulses is much higher than that of ambient light, thus improving the immunity of dTOF to ambient light and enabling dTOF to be used outdoors. In the field of autonomous driving, Niskanen et al tried to use a 2D TOF profiler to generate 3D point cloud maps for vehicles and achieved high accuracy [20]. However, this method has a low frame rate and can only be used for vehicles traveling at low speeds.…”

Section: Figure 3 Tof Schematic Diagrammentioning

confidence: 99%

Applications of visual perception techniques using neural networks in autonomous driving

2023

ACE

View full text Add to dashboard Cite

The perception system and the decision system are important components of a complete autonomous driving vehicle. The perception system can help the decision system to obtain the necessary information of external environment and vehicle status. The traditional perception system mainly relies on the on-board radar. But in recent years, vision-based perception techniques have become a hot research topic. Meanwhile, thanks to the excellent performance of neural networks in processing image data, the processing algorithms for visual perception images have also made great progress. Visual perception techniques can not only acquire more information, but also is more cost effective and easier to install. This paper provides an overview of the more mature and promising visual perception techniques, including their principles and data processing algorithms, in terms of acquiring 2D image data and 3D depth information. For acquiring 2D image data, this paper introduces the principle of event camera and reviews the current progress on the event camera. Regarding the acquisition of 3D depth information, three techniques are introduced, namely binocular stereo-vision, time of flight (TOF), and structured light. Their performance when combined with neural networks for autonomous driving applications is also reviewed. Finally, this paper lists the current dilemmas faced by the above 2D and 3D imaging techniques and the possible solutions.

show abstract

Section: Figure 3 Tof Schematic Diagrammentioning

confidence: 99%

Applications of visual perception techniques using neural networks in autonomous driving

2023

ACE

View full text Add to dashboard Cite

show abstract

“…Imaging and sensing techniques are low-cost reliable applications that have been implemented in recent years. The use of imaging and sensing techniques enable the processing, recording, and capturing of high-quality 3D images [75], providing semantically rich [68] digital information such as point clouds, 3D mesh models, and Digital Terrain Models [76]. When integrated with BIM, these 3D images allow for real-time monitoring of systems and processes and timely analysis of data.…”

Section: Imaging Technologiesmentioning

confidence: 99%

“…When integrated with BIM, these 3D images allow for real-time monitoring of systems and processes and timely analysis of data. Emerging imaging and sensing techniques that appeared in the literature review include radio detection and ranging (RADAR) [77], light detection and ranging (LiDAR) [54,58,76,78], terrestrial laser scanning (TLS) [55,56], time of flight (TOF) [75], photogrammetry [41,58,76], and global positioning systems (GPS) [24,41,69,76]. These technologies have been shown to support existing conditions modeling [76], 3D design of alternatives [76], clash detection [76], progress monitoring [58], virtual inspection [70,73], as-built surveys [76], and inventory mapping [76].…”

Section: Imaging Technologiesmentioning

confidence: 99%

Lifecycle Applications of Building Information Modeling for Transportation Infrastructure Projects

Belcher,

Abraham

2023

Buildings

View full text Add to dashboard Cite

Infrastructure plays a pivotal role in a nation’s economic stability, security, and long-term sustainability. For decades, United States infrastructure has been rated as inadequate and the resources required for its restoration continue to grow. Despite the transformative potential of Building Information Modeling (BIM) coupled with digital technologies in revolutionizing the building industry, its widespread adoption within infrastructure projects remains limited. This study explores the application of BIM and emerging technologies in transportation projects’ lifecycle, identifies research gaps, and discusses the use of technology to address current infrastructure challenges. Employing a systematic review approach, this research synthesizes BIM application studies based on well-defined criteria. The synthesis of the literature underscores the comprehensive integration of BIM throughout the lifecycle of transportation assets, catalyzed by collaborative, information-driven processes. However, challenges pertaining to interoperability, role delineation, standards enforcement, and cost persist. This study advocates for a deeper exploration of the synergy between BIM, digital technologies, and infrastructure, envisioning an enriched transportation lifecycle in the future.

show abstract

“…In addition, information intensity can be utilised to roughly identify sand and gravel materials and to recognise stockpiles using reflective surface markings. Furthermore, the 2D profilometer has been used in visualisation to capture the 4D point cloud information of vehicles at urban speeds (Niskanen et al 2021). This model facilitates road traffic flow monitoring in real-time and compared to traffic forecasting models; the model helps prevent congestion by optimising vehicle speeds.…”

Section: Introductionmentioning

confidence: 99%

Trench visualisation from a semiautonomous excavator with a base grid map using a TOF 2D profilometer

Niskanen

Immonen

Makkonen

et al. 2023

J Vis

Self Cite

View full text Add to dashboard Cite

Real-time, three-dimensional (3D) visualisation technology can be used at construction sites to improve the quality of work. A 3D view of the landscape under work can be compared to a target 3D model of the landscape to conveniently show needed excavation tasks to a human excavator operator or to show the progress of an autonomous excavator. The purpose of this study was to demonstrate surface visualisation from measurements taken with a pulsed time-of-flight (TOF) 2D profilometer on-board a semiautonomous excavator. The semiautomatic excavator was implemented by recording the feedback script parameters from the work performed on the excavator by a human driver. 3D visualisation maps based on the triangle mesh technique were generated from the 3D point cloud using measurements of the trenches dug by a human and an autonomous excavator. The accuracy of the 3D maps was evaluated by comparing them to a high-resolution commercial 3D scanner. An analysis of the results shows that the 2D profilometer attached to the excavator can achieve almost the same 3D results as a high-quality on-site static commercial 3D scanner, whilst more easily providing an unobstructed view of the trench during operation (a 3D scanner placed next to a deep trench might not have a full view of the trench). The main technical advantages of our 2D profilometer are its compact size, measurement speed, lack of moving parts, robustness, low-cost technology that enables visualisations from a unique viewpoint on the boom of the excavator, and readiness for real-time control of the excavator’s system. This research is expected to encourage the efficiency of the digging process in the future, as well as to provide a remarkable view of trench work using an excavator as a moving platform to facilitate data visualisation. Graphical abstract

show abstract

Time-of-flight sensor for getting shape model of automobiles toward digital 3D imaging approach of autonomous driving

Cited by 11 publications

References 10 publications

Applications of visual perception techniques using neural networks in autonomous driving

Applications of visual perception techniques using neural networks in autonomous driving

Lifecycle Applications of Building Information Modeling for Transportation Infrastructure Projects

Trench visualisation from a semiautonomous excavator with a base grid map using a TOF 2D profilometer

Contact Info

Product

Resources

About