Investigating the Impact of Multi-LiDAR Placement on Object Detection for Autonomous Driving

Hu, Hanjiang; Liu, Zuxin; Chitlangia, Sharad; Agnihotri, Akhil; Zhao, Ding

doi:10.1109/cvpr52688.2022.00258

Cited by 35 publications

(21 citation statements)

References 30 publications

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“…Descriptive radars serve three primary functions in autonomous driving. First, descriptive radars can be used for low-cost simulation experiments to optimize physical radar systems, such as new radar validation [46] and the optimal placement of multiple automotive radars [47]. Second, descriptive radars can help with the long-tail problem of data.…”

Section: B Descriptive Radarsmentioning

confidence: 99%

RadarVerses in Metaverses: A CPSI-Based Architecture for 6S Radar Systems in CPSS

Liu

Shen

Tian

et al. 2023

IEEE Trans. Syst. Man Cybern, Syst.

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Metaverses have caused significant changes in the industry and their academic foundation can be traced back to the term cyber-physical-social systems (CPSS), which was proposed in 2010. Radar is an important sensor in sensing systems that are widely applied in many fields, especially, in autonomous driving. To deal with the complex environment, smart radars with realtime information processing capabilities are required. Human factors play a critical role in the operation and management of radar systems, thus, digital twins' radars in cyber-physical systems (CPS) are unable to achieve intelligence in CPSS due to an incomplete consideration of human involvement. For this consideration, we propose a novel framework of RadarVerses for smart radars in metaverses based on ACP-based parallel intelligence, which is also known as cyber-physical-social intelligence (CPSI). RadarVerses consist of five main parts which are physical radars, descriptive radars, predictive radars, prescriptive radars, and deep radars. To construct RadarVerses at the technical level, we introduce four main technical foundations: 1) communication technology; 2) scenarios engineering; 3) foundation models; and 4) digital workers. In addition, we also provide a case study about LiDARs' predictive maintenance of accumulated snow in RadarVerses.

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Section: B Descriptive Radarsmentioning

confidence: 99%

RadarVerses in Metaverses: A CPSI-Based Architecture for 6S Radar Systems in CPSS

Liu

Shen

Tian

et al. 2023

IEEE Trans. Syst. Man Cybern, Syst.

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“…They used KITTI dataset to train the model. In another study [259], the researchers investigated how LiDARs placement affects on the performance of object detection models. They showed that sensor placement is significant in 3D point cloud-based object detection, contributing to 5% to 10% performance discrepancy.…”

Section: Cameramentioning

confidence: 99%

Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation -- A Comprehensive Review

Golroudbari¹,

Sabour²

2023

Preprint

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This review paper presents a comprehensive overview of end-to-end deep learning frameworks used in the context of autonomous navigation, including obstacle detection, scene perception, path planning, and control. The paper aims to bridge the gap between autonomous navigation and deep learning by analyzing recent research studies and evaluating the implementation and testing of deep learning methods. It emphasizes the importance of navigation for mobile robots, autonomous vehicles, and unmanned aerial vehicles, while also acknowledging the challenges due to environmental complexity, uncertainty, obstacles, dynamic environments, and the need to plan paths for multiple agents. The review highlights the rapid growth of deep learning in engineering data science and its development of innovative navigation methods. It discusses recent interdisciplinary work related to this field and provides a brief perspective on the limitations, challenges, and potential areas of growth for deep learning methods in autonomous navigation. Finally, the paper summarizes the findings and practices at different stages, correlating existing and future methods, their applicability, scalability, and limitations. The review provides a valuable resource for researchers and practitioners working in the field of autonomous navigation and deep learning.

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“…Directional sensors are those with explicit axes along which measurements are performed, for example joint torque sensors, strain gauges, accelerometers, gyroscopes, distance sensors, cameras, etc. While optimal sensor placement is an active area of research for mobile robots and sensor networks [1], [2], the same cannot be said for articulated and reconfigurable robots [3]. It is often a common assumption for articulated and reconfigurable robots that, given the presence of sensors, all task-space quantities are fully observable at all times.…”

Section: Introductionmentioning

confidence: 99%

Sensor Observability Index: Evaluating Sensor Alignment for Task-Space Observability in Robotic Manipulators

Wong

Suleiman

2022

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Investigating the Impact of Multi-LiDAR Placement on Object Detection for Autonomous Driving

Cited by 35 publications

References 30 publications

RadarVerses in Metaverses: A CPSI-Based Architecture for 6S Radar Systems in CPSS

RadarVerses in Metaverses: A CPSI-Based Architecture for 6S Radar Systems in CPSS

Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation -- A Comprehensive Review

Sensor Observability Index: Evaluating Sensor Alignment for Task-Space Observability in Robotic Manipulators

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