Model-Based Derivation of Perception Accuracy Requirements for Vehicle Localization in Urban Environments

We propose an approach for vehicle localization in dense urban environments using a stereoscopic system and a GPS sensor. Stereoscopic system is used to capture the stereo video flow, to recover the environments, and to estimate the vehicle motion based on feature detection, matching, and triangulation from every image pair. A relative depth constraint is applied to eliminate the tracking couples which are inconsistent with the vehicle ego-motion. Then the optimal rotation and translation between the current and the reference frames are computed using an RANSAC based minimization method. Meanwhile, GPS positions are obtained by an on-board GPS receiver and periodically used to adjust the vehicle orientations and positions estimated by stereovision. The proposed method is tested with two real sequences obtained by a GEM vehicle equipped with a stereoscopic system and a RTK-GPS receiver. The results show that the vision/GPS integrated trajectory can fit the ground truth better than the vision-only method, especially for the vehicle orientation. And vice-versa, the stereovision-based motion estimation method can correct the GPS signal failures (e.g., GPS jumps) due to multipath problem or other noises.

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Assessment of modern smartphone sensors performance on vehicle localization in urban environments

Fifth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2017) ◽

10.1117/12.2292354 ◽

2017 ◽

Author(s):

Chris Danezis ◽

Theodoros Lazarou

Keyword(s):

Urban Environments ◽

Vehicle Localization ◽

Smartphone Sensors

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Experimental Validation of Gaussian Process-Based Air-to-Ground Communication Quality Prediction in Urban Environments

Sensors ◽

10.3390/s19143221 ◽

2019 ◽

Vol 19 (14) ◽

pp. 3221 ◽

Cited By ~ 1

Author(s):

Pawel Ladosz ◽

Jongyun Kim ◽

Hyondong Oh ◽

Wen-Hua Chen

Keyword(s):

Gaussian Process ◽

Urban Environment ◽

Urban Environments ◽

World City ◽

Model Parameters ◽

Communication Model ◽

Channel Prediction ◽

Model Based ◽

Communication Signal ◽

Prediction Approach

This paper presents a detailed experimental assessment of Gaussian Process (GP) regression for air-to-ground communication channel prediction for relay missions in urban environment. Considering restrictions from outdoor urban flight experiments, a way to simulate complex urban environments at an indoor room scale is introduced. Since water significantly absorbs wireless communication signal, water containers are utilized to replace buildings in a real-world city. To evaluate the performance of the GP-based channel prediction approach, several indoor experiments in an artificial urban environment were conducted. The performance of the GP-based and empirical model-based prediction methods for a relay mission was evaluated by measuring and comparing the communication signal strength at the optimal relay position obtained from each method. The GP-based prediction approach shows an advantage over the model-based one as it provides a reasonable performance without a need for a priori information of the environment (e.g., 3D map of the city and communication model parameters) in dynamic urban environments.

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