LiDAR SLAM Based Multivehicle Cooperative Localization Using Iterated Split CIF

Fang, Susu; Li, Hao; Yang, Ming

doi:10.1109/tits.2022.3174479

Cited by 27 publications

(5 citation statements)

References 44 publications

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“…• An oracle version of the proposed method, referred to as O-CLS-MPNN, with perfect DA and LiDAR sensing where all poles present in the field of view of the LiDAR sensor are detected. • A state-of-the-art cooperative SLAM approach [40] that requires vehicles to exchange their positions and the raw LiDAR point clouds, denoted as C-SLAM. 1) Cooperative localization performances: Fig.…”

Section: A Town02 Resultsmentioning

confidence: 99%

“…The works in [37], [38] investigate the association issue across multiple vehicles, whereas [39] develops a decentralized cooperative localization method to fuse LiDAR, GNSS and high-definition maps. In [40], a cooperative Simultaneous Localization and Mapping (SLAM) approach is developed for vehicle pose estimation, while [41] proposes a cooperative mapping technique to fuse multiple LiDAR views into an improved unified map. Similarly, also [42] develops a multi-vehicle cooperative scheme where vehicles share locally processed maps to improve cooperative perception.…”

Section: A Related Workmentioning

confidence: 99%

“…With respect to previous works that either rely on partially processed [43]- [45] or raw LiDAR sharing [40], [41], the proposed method foresees only the exchange of the detected objects and vehicles positions, thereby substantially limiting the communication overhead. Furthermore, the developed method does not rely on maps of the driving environment, as in [39], making the cooperative approach potentially applicable to any driving situation, especially when high-definition maps are unavailable.…”

Section: B Contributionsmentioning

confidence: 99%

See 2 more Smart Citations

Deep Learning-Based Cooperative LiDAR Sensing for Improved Vehicle Positioning

Barbieri,

Tedeschini,

Brambilla

et al. 2024

IEEE Trans. Signal Process.

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Accurate positioning is known to be a fundamental requirement for the deployment of Connected Automated Vehicles (CAVs). To meet this need, a new emerging trend is represented by cooperative methods where vehicles fuse information coming from navigation and imaging sensors via Vehicleto-Everything (V2X) communications for joint positioning and environmental perception. In line with this trend, this paper proposes a novel data-driven cooperative sensing framework, termed Cooperative LiDAR Sensing with Message Passing Neural Network (CLS-MPNN), where spatially-distributed vehicles collaborate in perceiving the environment via LiDAR sensors. Vehicles process their LiDAR point clouds using a Deep Neural Network (DNN), namely a 3D object detector, to identify and localize possible static objects present in the driving environment. Data are then aggregated by a centralized infrastructure that performs Data Association (DA) using a Message Passing Neural Network (MPNN) and runs the Implicit Cooperative Positioning (ICP) algorithm. The proposed approach is evaluated using two realistic driving scenarios generated by a high-fidelity automated driving simulator. The results show that CLS-MPNN outperforms a conventional non-cooperative localization algorithm based on Global Navigation Satellite System (GNSS) and a state-of-theart cooperative Simultaneous Localization and Mapping (SLAM) method while approaching the performances of an oracle system with ideal sensing and perfect association.

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Section: A Town02 Resultsmentioning

confidence: 99%

Section: A Related Workmentioning

confidence: 99%

Section: B Contributionsmentioning

confidence: 99%

See 1 more Smart Citation

Deep Learning-Based Cooperative LiDAR Sensing for Improved Vehicle Positioning

Barbieri,

Tedeschini,

Brambilla

et al. 2024

IEEE Trans. Signal Process.

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“…Yuan et al [42] proposed a 3D keypoints feature fusion scheme for cooperative driving detection to remedy the problem of low bounding box localization accuracy. Fang et al [43] presented an iterated split covariance intersection filter-based cooperative localization strategy with a decentralized framework. In addition, they adopted a point cloud registration method to obtain the relative pose estimation using mutually shared information from neighbour vehicles.…”

Section: Previous Workmentioning

confidence: 99%

Cooperative Saliency-Based Pothole Detection and AR Rendering for Increased Situational Awareness

Arvanitis,

Stagakis,

Zacharaki

et al. 2024

IEEE Trans. Intell. Transport. Syst.

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Autonomous vehicles are expected to operate safely in real-life road conditions in the next years. Nevertheless, unanticipated events such as the existence of unexpected objects in the range of the road, can put safety at risk. The advancement of sensing and communication technologies and Internet of Things may facilitate the recognition of hazardous situations and information exchange in a cooperative driving scheme, providing new opportunities for the increase of collaborative situational awareness. Safe and unobtrusive visualization of the obtained information may nowadays be enabled through the adoption of novel Augmented Reality (AR) interfaces in the form of windshields. Motivated by these technological opportunities, we propose a saliency-based distributed, cooperative and rendering scheme for increasing the driver's situational awareness through (i) automated negative obstacle (potholes) detection, (ii) AR visualization and (iii) information sharing (upcoming potential dangers) with other connected vehicles or road infrastructure. An extensive evaluation study using a variety of real datasets for pothole detection showed that the proposed method provides favorable results and features compared to other recent and relevant approaches.

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“…In the decentralization mechanism, there may simultaneously exist more than one type of error sources. In addition to outliers, there are also errors caused by the correlation between estimates [36]. For multirobot localization systems, underestimation or even neglect of the correlations between estimates can cause circular reasoning, which further leads to the over-convergence problem [37], [38].…”

Section: Introductionmentioning

confidence: 99%