Zobrazeno 1 - 10
of 119
pro vyhledávání: '"Fremont, Vincent"'
In autonomous robotics, a significant challenge involves devising robust solutions for Active Collaborative SLAM (AC-SLAM). This process requires multiple robots to cooperatively explore and map an unknown environment by intelligently coordinating th
Externí odkaz:
http://arxiv.org/abs/2407.05453
Autor:
Dao, Minh-Quan, Caesar, Holger, Berrio, Julie Stephany, Shan, Mao, Worrall, Stewart, Frémont, Vincent, Malis, Ezio
Occlusion presents a significant challenge for safety-critical applications such as autonomous driving. Collaborative perception has recently attracted a large research interest thanks to the ability to enhance the perception of autonomous vehicles v
Externí odkaz:
http://arxiv.org/abs/2404.06256
In this article, we present an efficient multi-robot active SLAM framework that involves a frontier-sharing method for maximum exploration of an unknown environment. It encourages the robots to spread into the environment while weighting the goal fro
Externí odkaz:
http://arxiv.org/abs/2310.06160
Publikováno v:
Conference: 2023 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI)
In this article we present a utility function for Active SLAM (A-SLAM) which utilizes map entropy along with D-Optimality criterion metrices for weighting goal frontier candidates. We propose a utility function for frontier goal selection that exploi
Externí odkaz:
http://arxiv.org/abs/2309.16490
Practical Collaborative Perception: A Framework for Asynchronous and Multi-Agent 3D Object Detection
Autor:
Dao, Minh-Quan, Berrio, Julie Stephany, Frémont, Vincent, Shan, Mao, Héry, Elwan, Worrall, Stewart
Occlusion is a major challenge for LiDAR-based object detection methods. This challenge becomes safety-critical in urban traffic where the ego vehicle must have reliable object detection to avoid collision while its field of view is severely reduced
Externí odkaz:
http://arxiv.org/abs/2307.01462
Low-resolution point clouds are challenging for object detection methods due to their sparsity. Densifying the present point cloud by concatenating it with its predecessors is a popular solution to this challenge. Such concatenation is possible thank
Externí odkaz:
http://arxiv.org/abs/2305.02909
Publikováno v:
Sensors 2023, 23, 8097
This article presents a comprehensive review of the Active Simultaneous Localization and Mapping (A-SLAM) research conducted over the past decade. It explores the formulation, applications, and methodologies employed in A-SLAM, particularly in trajec
Externí odkaz:
http://arxiv.org/abs/2212.11654
Event-based cameras can overpass frame-based cameras limitations for important tasks such as high-speed motion detection during self-driving cars navigation in low illumination conditions. The event cameras' high temporal resolution and high dynamic
Externí odkaz:
http://arxiv.org/abs/2201.12265
Recent advances in 3D object detection are made by developing the refinement stage for voxel-based Region Proposal Networks (RPN) to better strike the balance between accuracy and efficiency. A popular approach among state-of-the-art frameworks is to
Externí odkaz:
http://arxiv.org/abs/2201.07070
Autor:
Dao, Minh-Quan, Frémont, Vincent
Multi-object tracking (MOT) is an integral part of any autonomous driving pipelines because itproduces trajectories which has been taken by other moving objects in the scene and helps predicttheir future motion. Thanks to the recent advances in 3D ob
Externí odkaz:
http://arxiv.org/abs/2101.08684