Pub Date : 2025-06-27DOI: 10.1109/TIV.2024.3496639
{"title":"Proceedings of the IEEE","authors":"","doi":"10.1109/TIV.2024.3496639","DOIUrl":"https://doi.org/10.1109/TIV.2024.3496639","url":null,"abstract":"","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"9 11","pages":"7546-7546"},"PeriodicalIF":14.0,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11054038","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144511193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-27DOI: 10.1109/TIV.2025.3583551
Gilhwan Kang;Hogyun Kim;Byunghee Choi;Seokhwan Jeong;Young-Sik Shin;Younggun Cho
The unification of disparate maps is crucial for enabling scalable robot operation across multi-session, multi-robot scenarios. However, achieving a unified map robust to sensor modalities and dynamic environments remains a challenging problem. Variations in LiDAR types and dynamic elements lead to differences in point cloud distribution and scene consistency, hindering reliable descriptor generation and loop closure detection essential for accurate map alignment. To address these challenges, this paper presents Uni-Mapper, a dynamic-aware 3D point cloud map merging framework for multi-modal LiDAR systems. It comprises dynamic object removal, dynamic-aware loop closure, and multi-modal LiDAR map merging modules. A voxel-wise free space hash map is built in a coarse-to-fine manner to identify and reject dynamic objects via temporal occupancy inconsistencies. The removal module is integrated with a LiDAR global descriptor, which encodes preserved static local features to ensure robust place recognition in dynamic environments. In the final stage, centralized anchor-node-based pose graph optimization is performed to address intra- and inter-map loop closures for globally consistent map merging. Our framework is evaluated on diverse real-world datasets with dynamic objects and heterogeneous LiDARs, showing superior performance in loop detection across sensor modalities, robust mapping in dynamic environments, and accurate multi-map alignment over existing methods.
{"title":"Uni-Mapper: Unified Mapping Framework for Multi-Modal LiDARs in Complex and Dynamic Environments","authors":"Gilhwan Kang;Hogyun Kim;Byunghee Choi;Seokhwan Jeong;Young-Sik Shin;Younggun Cho","doi":"10.1109/TIV.2025.3583551","DOIUrl":"https://doi.org/10.1109/TIV.2025.3583551","url":null,"abstract":"The unification of disparate maps is crucial for enabling scalable robot operation across multi-session, multi-robot scenarios. However, achieving a unified map robust to sensor modalities and dynamic environments remains a challenging problem. Variations in LiDAR types and dynamic elements lead to differences in point cloud distribution and scene consistency, hindering reliable descriptor generation and loop closure detection essential for accurate map alignment. To address these challenges, this paper presents <italic>Uni-Mapper</i>, a dynamic-aware 3D point cloud map merging framework for multi-modal LiDAR systems. It comprises dynamic object removal, dynamic-aware loop closure, and multi-modal LiDAR map merging modules. A voxel-wise free space hash map is built in a coarse-to-fine manner to identify and reject dynamic objects via temporal occupancy inconsistencies. The removal module is integrated with a LiDAR global descriptor, which encodes preserved static local features to ensure robust place recognition in dynamic environments. In the final stage, centralized anchor-node-based pose graph optimization is performed to address intra- and inter-map loop closures for globally consistent map merging. Our framework is evaluated on diverse real-world datasets with dynamic objects and heterogeneous LiDARs, showing superior performance in loop detection across sensor modalities, robust mapping in dynamic environments, and accurate multi-map alignment over existing methods.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"11 1","pages":"104-121"},"PeriodicalIF":14.3,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145802382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In complex environments, decision-making for an Intelligent Vehicle (IV) requires reliability to ensure safe and efficient navigation. During vehicle operation, context and vehicle's capabilities influence the feasibility of its possible decisions and actions functions. Context-awareness allows to enhance the reliability of the decision-making process, ensuring that the vehicle has the capabilities to effectively perform the desired action. To warrant the safe operation of a vehicle, the Operational Design Domain (ODD) concept has been introduced. In the literature, it defines the conditions under which the vehicle is designed to operate safely. In this survey the ODD concept serves as a formalism to describe the context according to an established taxonomy. This survey focuses on how the operational context and the vehicle's capabilities determine the manner of how decisions are taken to ensure driving safety, comfort, and reliability. This is a multidimensional problem as the vehicle's capabilities, the road, the road users, and other elements of the context need to be considered. The different approaches and methods used in decision-making for IVs which take into account contextual information are identified as well as the research gaps that still need to be addressed in order to ensure reliable decision-making. Further, recent approaches that consider the ODD framework are presented to highlight the importance of this formalism. Conclusions underscore the importance of this integration for IVs and offer key insights for future research, emphasizing the crucial synergy between reliable long-term decision-making and the ODD as a contextual-awareness formalism.
{"title":"Context-Aware and Reliable Long-Term Decision-Making for Safe Intelligent Vehicles: A Survey","authors":"Rhandy Cardenas;Lounis Adouane;Clément Zinoune;Mohamed-Amir Benloucif","doi":"10.1109/TIV.2024.3524881","DOIUrl":"https://doi.org/10.1109/TIV.2024.3524881","url":null,"abstract":"In complex environments, decision-making for an Intelligent Vehicle (IV) requires reliability to ensure safe and efficient navigation. During vehicle operation, context and vehicle's capabilities influence the feasibility of its possible decisions and actions functions. Context-awareness allows to enhance the reliability of the decision-making process, ensuring that the vehicle has the capabilities to effectively perform the desired action. To warrant the safe operation of a vehicle, the Operational Design Domain (ODD) concept has been introduced. In the literature, it defines the conditions under which the vehicle is designed to operate safely. In this survey the ODD concept serves as a formalism to describe the context according to an established taxonomy. This survey focuses on how the operational context and the vehicle's capabilities determine the manner of how decisions are taken to ensure driving safety, comfort, and reliability. This is a multidimensional problem as the vehicle's capabilities, the road, the road users, and other elements of the context need to be considered. The different approaches and methods used in decision-making for IVs which take into account contextual information are identified as well as the research gaps that still need to be addressed in order to ensure reliable decision-making. Further, recent approaches that consider the ODD framework are presented to highlight the importance of this formalism. Conclusions underscore the importance of this integration for IVs and offer key insights for future research, emphasizing the crucial synergy between reliable long-term decision-making and the ODD as a contextual-awareness formalism.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"10 12","pages":"5288-5307"},"PeriodicalIF":14.3,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145772067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：