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Related Experiment Video

Updated: Dec 28, 2025

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Published on: October 27, 2023

Stabilization and Validation of 3D Object Position Using Multimodal Sensor Fusion and Semantic Segmentation.

Mircea Paul Mureșan¹, Ion Giosan¹, Sergiu Nedevschi¹

¹Computer Science Department, Technical University of Cluj-Napoca, 28 Memorandumului Street, 400114 Cluj Napoca, Romania.

Sensors (Basel, Switzerland)

|February 23, 2020

Summary

This study introduces new methods for stabilizing and validating object positions in autonomous vehicles using multiple sensors. The techniques improve accuracy and reliability in sensor fusion for enhanced perception.

Keywords:

data association motion compensation multi-object tracking neural networks sensor fusion

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Area of Science:

Robotics and Autonomous Systems
Computer Vision
Sensor Fusion

Background:

Accurate object position stabilization and validation are crucial for autonomous vehicle perception.
Sensor fusion aggregates data from multiple sensors but can introduce errors like false detections and misaligned object representations.
Existing methods require robust solutions to mitigate these perception system inconsistencies.

Purpose of the Study:

To propose novel algorithms for the stabilization and validation of object positions in autonomous vehicles.
To enhance sensor fusion by addressing inconsistencies arising from diverse sensor inputs (trifocal camera, fisheye camera, RADAR, LIDAR).
To improve the accuracy and reliability of object detection and tracking in complex driving scenarios.

Main Methods:

Developed two novel data association algorithms: one for LIDAR object tracking using appearance and motion features, and another for trifocal camera object association incorporating semantic class information via a polar scheme and decision tree.
Implemented a fusion approach using the Unscented Kalman Filter and a single-layer perceptron for stabilizing object positions and handling unpredictable road object behavior.
Utilized a fuzzy logic technique combined with semantic segmentation images for validating 3D object positions.

Main Results:

Achieved real-time performance with a cumulative running time of 90 ms.
Evaluated algorithms using high-precision GPS ground truth data (2 cm accuracy).
Obtained an average position error of 0.8 m, demonstrating significant improvement in object position accuracy.

Conclusions:

The proposed methods effectively stabilize and validate object positions, enhancing the reliability of sensor fusion for autonomous vehicles.
The novel data association and fusion techniques contribute to more accurate and robust perception systems.
The real-time performance and high accuracy validate the practical applicability of these solutions in autonomous driving.