Vehicle-to-Vehicle Collaborative Graph-Based Proprioceptive Localization

Cheng, Hsin-Min; Chou, Chieh; Song, Dezhen

doi:10.1109/lra.2021.3056032

Cited by 4 publications

(3 citation statements)

References 27 publications

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“…Ref. [43] utilizes V2V communication technology for multi-vehicle collaborative positioning. When two vehicles converge, a merged query sequence is formed and matched with the map to achieve precise positioning.…”

Section: Related Workmentioning

confidence: 99%

Road-Network-Map-Assisted Vehicle Positioning Based on Pose Graph Optimization

Xu,

Sun,

Zhao

et al. 2023

Sensors

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Satellite signals are easily lost in urban areas, which causes difficulty in vehicles being located with high precision. Visual odometry has been increasingly applied in navigation systems to solve this problem. However, visual odometry relies on dead-reckoning technology, where a slight positioning error can accumulate over time, resulting in a catastrophic positioning error. Thus, this paper proposes a road-network-map-assisted vehicle positioning method based on the theory of pose graph optimization. This method takes the dead-reckoning result of visual odometry as the input and introduces constraints from the point-line form road network map to suppress the accumulated error and improve vehicle positioning accuracy. We design an optimization and prediction model, and the original trajectory of visual odometry is optimized to obtain the corrected trajectory by introducing constraints from map correction points. The vehicle positioning result at the next moment is predicted based on the latest output of the visual odometry and corrected trajectory. The experiments carried out on the KITTI and campus datasets demonstrate the superiority of the proposed method, which can provide stable and accurate vehicle position estimation in real-time, and has higher positioning accuracy than similar map-assisted methods.

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Section: Related Workmentioning

confidence: 99%

Road-Network-Map-Assisted Vehicle Positioning Based on Pose Graph Optimization

Xu,

Sun,

Zhao

et al. 2023

Sensors

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show abstract

“…While our method of using pose graphs and correcting them with information from other vehicles is similar, our method is not constrained to a previously-known map. Note that both this paper and [29] are based on two-vehicle rendezvous, and that both works are similarly extensible to n-vehicle rendezvous by way of n − 1 two-vehicle rendezvous.…”

Section: Related Workmentioning

confidence: 99%

“…In [29] a graph-based collaborative localization scheme is described for autonomous cars. The method makes use of a previously-known graph that represents the roads and intersections of the city and aims to ascertain the location of a vehicle on this graph given its own measurements and any information the ego vehicle acquires from other vehicles.…”

Section: Related Workmentioning

confidence: 99%

Collaborative Navigation-Aware Coverage in Feature-Poor Environments

Özkahraman¹,

Ögren²

2022

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Multi agent coverage and robot navigation are two very important research fields within robotics. However, their intersection has received limited attention. In multi agent coverage, perfect navigation is often assumed, and in robot navigation, the focus is often to minimize the localization error with the aid of stationary features from the environment. The need for integration of the two becomes clear in environments with very sparse features or landmarks, for example when a group of Autonomous Underwater Vehicles (AUVs) are to search a uniform seafloor for mines or other dangerous objects. In such environments, localization systems are often deprived of detectable features to use that could increase their accuracy. In this paper we propose an algorithm for doing navigation aware multi agent coverage in areas with no landmarks. Instead of using identical lawn mower patterns, we propose to mirror every other pattern to enable the agents to meet up and make inter-agent measurements and share information regularly. This improves performance in two ways, global drift in relation to the area to be covered is reduced, and local coverage gaps between adjacent patterns are reduced. Further, we show that this can be accomplished within the constraints of very limited sensing, computing and communication resources that most AUVs have available. The effectiveness of our method is shown through statistically significant simulated experiments.

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