What is the best grid-map for self-driving cars localization? An evaluation under diverse types of illumination, traffic, and environment

Mutz, Filipe; Oliveira-Santos, Thiago; Forechi, Avelino; Komati, Karin Satie; Badué, Claudine; França, Felipe M. G.; Souza, Alberto F. De

doi:10.1016/j.eswa.2021.115077

Cited by 6 publications

(4 citation statements)

References 41 publications

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“…The grid map also shows the potential for detecting moving objects [187]. Mutz et al [188] compare the performance of mapping and localization with different grid maps, including occupancy, reflectivity, color, and semantic grid maps, for self-driving car applications in diverse driving environments, including under challenging conditions. GraphSLAM was used for mapping, while localization was based on particle filter solutions.…”

Section: Representing the Environmentmentioning

confidence: 99%

Simultaneous Localization and Mapping (SLAM) for Autonomous Driving: Concept and Analysis

et al. 2023

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The Simultaneous Localization and Mapping (SLAM) technique has achieved astonishing progress over the last few decades and has generated considerable interest in the autonomous driving community. With its conceptual roots in navigation and mapping, SLAM outperforms some traditional positioning and localization techniques since it can support more reliable and robust localization, planning, and controlling to meet some key criteria for autonomous driving. In this study the authors first give an overview of the different SLAM implementation approaches and then discuss the applications of SLAM for autonomous driving with respect to different driving scenarios, vehicle system components and the characteristics of the SLAM approaches. The authors then discuss some challenging issues and current solutions when applying SLAM for autonomous driving. Some quantitative quality analysis means to evaluate the characteristics and performance of SLAM systems and to monitor the risk in SLAM estimation are reviewed. In addition, this study describes a real-world road test to demonstrate a multi-sensor-based modernized SLAM procedure for autonomous driving. The numerical results show that a high-precision 3D point cloud map can be generated by the SLAM procedure with the integration of Lidar and GNSS/INS. Online four–five cm accuracy localization solution can be achieved based on this pre-generated map and online Lidar scan matching with a tightly fused inertial system.

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Section: Representing the Environmentmentioning

confidence: 99%

Simultaneous Localization and Mapping (SLAM) for Autonomous Driving: Concept and Analysis

et al. 2023

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“…OGMs play an important role in industrial robotic motion planning applications as depicted in the supported robotic platforms webpage of MoveIt [22], especially in cooperative scenarios in which operators share the same space [23]. They are also used extensively in the self-driving car industry, since they can deal with a wide variety of environments under diverse conditions of illumination and traffic [24], addressing problems such as road boundary detection [25]. In this domain performance is a critical factor and methods tend to take full advantage of GPU implementations [26].…”

Section: A Applicationsmentioning

confidence: 99%

OHM: GPU Based Occupancy Map Generation

Stepanas¹,

Williams²,

Hernández³

et al. 2022

Preprint

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Occupancy grid maps (OGMs) are fundamental to most systems for autonomous robotic navigation. However, CPU-based implementations struggle to keep up with data rates from modern 3D lidar sensors, and provide little capacity for modern extensions which maintain richer voxel representations. This paper presents OHM, our open source, GPU-based OGM framework. We show how the algorithms can be mapped to GPU resources, resolving difficulties with contention to obtain a successful implementation. The implementation supports many modern OGM algorithms including NDT-OM, NDT-TM, decay-rate and TSDF. A thorough performance evaluation is presented based on tracked and quadruped UGV platforms and UAVs, and data sets from both outdoor and subterranean environments. The results demonstrate excellent performance improvements both offline, and for online processing in embedded platforms. Finally, we describe how OHM was a key enabler for the UGV navigation solution for our entry in the DARPA Subterranean Challenge, which placed second at the Final Event.

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“…However, consumer awareness and understanding will be key to ensuring self‐driving cars continue to expand their share of mainstream market sales. Research to date has focused mainly on the function‐related antecedents of self‐driving car development decisions (Deruyttere et al, 2021; Mohammadi et al, 2023; Muthalagu et al, 2020; Mutz et al, 2021), with little attention given in the literature to consumer awareness, understanding, or feedback regarding these vehicles. Therefore, the main purpose of this study is to investigate the key factors influencing Taiwanese consumer interest in purchasing self‐driving cars.…”

Section: Introductionmentioning

confidence: 99%

What drives consumers' intention to purchase self‐driving cars

Wang,

Chiu,

Wei

et al. 2024

Manage Decis Econ

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This study endeavors to examine the factors influencing consumers' intentions to purchase self‐driving cars. The data collected from 174 respondents underwent analysis using the partial least squares technique. The findings reveal that relative advantage, compatibility, trialability, and observability significantly impact consumer attitudes. Additionally, consumer attitude, subjective norm, and perceived behavioral control were identified as significant influencers of the intention to purchase a self‐driving car. Furthermore, achievement vanity was observed to moderate the effect of observability on consumer attitude. These results offer critical theoretical insights and practical implications for the adoption of self‐driving cars.

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What is the best grid-map for self-driving cars localization? An evaluation under diverse types of illumination, traffic, and environment

Cited by 6 publications

References 41 publications

Simultaneous Localization and Mapping (SLAM) for Autonomous Driving: Concept and Analysis

Simultaneous Localization and Mapping (SLAM) for Autonomous Driving: Concept and Analysis

OHM: GPU Based Occupancy Map Generation

What drives consumers' intention to purchase self‐driving cars

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