Adaptive and soft constrained vision-map vehicle localization using Gaussian processes and instance segmentation

doi:10.1016/j.eswa.2024.125790

Adaptive and soft constrained vision-map vehicle localization using Gaussian processes and instance segmentation

2025-03-01

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Bruno Henrique Groenner Barbosa *

DOI：10.1016/j.eswa.2024.125790

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The accurate and reliable localization is crucial for the safe performance of autonomous vehicles in uncertain and complex environments such as urban areas. Thus, this paper contributes by presenting a feature-based localization framework for autonomous vehicles where segmented map features are used for designing soft and adaptive constraints for Particle Filtering. After obtaining features of landmarks (light poles) in instance- based segmented images acquired from a monocular camera, vehicle-to-landmark distances are predicted using Gaussian Process Regression models (GPR). Both mean and variance (uncertainty) outputs of Gaussian Process models were used to implement adaptive soft constraints. Experimental results over the WATonoBus from the University of Waterloo confirm that the use of the proposed constraints improves the vehicle states estimation. Using instance segmentation increases the vehicle-to-landmark distance prediction accuracy by 27% and keeps the real-time processing capability of the proposed approach. Besides, the proposed approach reliably localizes the vehicle in very noisy conditions using only a monocular camera, even with a reduced number of landmarks, reducing the localization Euclidean error from 2.55 m to 0.97 m when compared to an unconstrained Particle Filter.

Keywords:

Map-based localization

Monocular vision

Landmark segmentation

Gaussian process

Constrained particle filter

Autonomous vehicle

Adaptive and soft constrained vision-map vehicle localization using Gaussian processes and instance segmentation

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