Collaborative Localization for Occluded Objects in Connected Vehicular Platform

Guo, Rui; Lu, Hongsheng; Gao, Peng; Zhang, Ziling; Zhang, Hao

doi:10.1109/vtcfall.2019.8891157

Cited by 13 publications

(5 citation statements)

References 26 publications

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“…Inspired by the existing work [6], [21], [24], our work extends the hyper-graph matching by combing multiple similarity measures, including attribute-based similarity measures, geometry-based similarity measures, etc., in matching. Each vehicle can first build a hyper-graph based on its locally detected objects, and additionally, it can also build a hyper-graph for a remote vehicle based on the shared information from that vehicle.…”

Section: Hyper-graph Matching With Multiple Similarity Measuresmentioning

confidence: 99%

Bridging the Gap Between Point Cloud Registration and Connected Vehicles

Liu

et al. 2022

IEEE Open J. Veh. Technol.

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Connected vehicles can benefit from sharing and merging their observations to develop a more complete understanding of the traffic scene and track traffic participants behind obstructions. Although vehicle-to-vehicle(V2V) communications provide a channel for point cloud data sharing, it is challenging to align point clouds from two vehicles with state-of-the-art techniques due to localization errors, visual obstructions, and differences in perspective. Therefore, we propose a two-phase point cloud registration mechanism to fuse point clouds which focuses on key objects in the scene where the point clouds are most similar and infer the transformation from those. Our system first identifies co-visible objects between vehicle views based on hyper-graph matching using multiple similarity metrics, and then refines the overlap region between co-visible objects across the views for point cloud registration. The system is evaluated based on both experimental and simulation data, which shows tremendous performance improvement when combing with state-of-art baselines.

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Section: Hyper-graph Matching With Multiple Similarity Measuresmentioning

confidence: 99%

Bridging the Gap Between Point Cloud Registration and Connected Vehicles

Liu

et al. 2022

IEEE Open J. Veh. Technol.

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“…Collaborative perception is a fundamental capability in collaborative robotics for robots and other agents including humans in a collaborative team to share information of the surrounding environment thus achieving shared situational awareness among the teammates. Collaborative perception has been widely applied in a variety of real-world applications including human-robot collaborative assembly [18,20], multi-robot search and rescue [1,45], and connected autonomous driving [19,49]. Correspondence identification is defined as a problem to identify the same objects observed by multiple agents in their own fields of view, which is considered an essential component to enable collaborative perception [14,17,43].…”

Section: Introductionmentioning

confidence: 99%

Bayesian Deep Graph Matching for Correspondence Identification in Collaborative Perception

Gao¹,

Zhang²

2021

Robotics: Science and Systems XVII

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Correspondence identification is essential for multirobot collaborative perception, which aims to identify the same objects in order to ensure consistent references of the objects by a group of robots/agents in their own fields of view. Although recent deep learning methods have shown encouraging performance on correspondence identification, they suffer from two shortcomings, including the inability to address non-covisibility in collaborative perception that is caused by occlusion and limited fields of view of the agents, and the inability to quantify and reduce uncertainty to improve correspondence identification. To address both issues, we propose a novel uncertainty-aware deep graph matching method for correspondence identification in collaborative perception. Our new approach formulates correspondence identification as a deep graph matching problem, which identifies correspondences based upon graph representations that are constructed from the agents' observations. We introduce a novel deep graph matching network under the Bayesian framework to explicitly quantify uncertainty in the identified correspondences. In addition, we design a novel loss function that explicitly reduces correspondence uncertainty and perceptual non-covisibility during learning. We evaluate our approach in the robotics applications of collaborative assembly and multi-robot coordination using high-fidelity simulations and physical robots. Experiments have shown that, through addressing both uncertainty and non-covisibility, our approach achieves the state-of-the-art performance of correspondence identification.

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“…Recently, collaborative object localization using a team of robots has attracted an increased interest because of its improved object localization accuracy and resilience to sensor failures [5]. The goal of collaborative object localization is to estimate locations of observed objects by fusing observations obtained by multiple robots from different perspectives [6], [7], [8], [9]. For example, as shown in Figure 1, two connected vehicles are able to improve shared situational awareness and decrease blind spots at an intersection by combining their observations to collaboratively localize street objects.…”

Section: Introductionmentioning

confidence: 99%

Asynchronous Collaborative Localization by Integrating Spatiotemporal Graph Learning with Model-Based Estimation

Gao¹,

Reily²,

Guo³

et al. 2021

Preprint

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Collaborative localization is an essential capability for a team of robots such as connected vehicles to collaboratively estimate object locations from multiple perspectives with reliant cooperation. To enable collaborative localization, four key challenges must be addressed, including modeling complex relationships between observed objects, fusing observations from an arbitrary number of collaborating robots, quantifying localization uncertainty, and addressing latency of robot communications. In this paper, we introduce a novel approach that integrates uncertainty-aware spatiotemporal graph learning and modelbased state estimation for a team of robots to collaboratively localize objects. Specifically, we introduce a new uncertaintyaware graph learning model that learns spatiotemporal graphs to represent historical motions of the objects observed by each robot over time and provides uncertainties in object localization. Moreover, we propose a novel method for integrated learning and model-based state estimation, which fuses asynchronous observations obtained from an arbitrary number of robots for collaborative localization. We evaluate our approach in two collaborative object localization scenarios in simulations and on real robots. Experimental results show that our approach outperforms previous methods and achieves state-of-the-art performance on asynchronous collaborative localization.

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Collaborative Localization for Occluded Objects in Connected Vehicular Platform

Cited by 13 publications

References 26 publications

Bridging the Gap Between Point Cloud Registration and Connected Vehicles

Bridging the Gap Between Point Cloud Registration and Connected Vehicles

Bayesian Deep Graph Matching for Correspondence Identification in Collaborative Perception

Asynchronous Collaborative Localization by Integrating Spatiotemporal Graph Learning with Model-Based Estimation

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