Enhancing Odometry Estimation for Mobile Ground Vehicles: Leveraging Wheel Velocity Measurements and the Kalman Filter

This paper delves into the initial phase of developing an autonomous navigation system for mobile platforms. It examines the impact of wheel velocity measurements on odometry estimation, considering the transition from a reliable and precise signal to noisy and less dependable information. The study...

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Main Authors: Rozo-Osorio, David, Nunez-Lopez, Juan D., Lopez-Gonzalez, Alexandro, Ramirez-Neria, Mario, Tejada, Juan C.
Format: Conference Proceeding
Language:English
Subjects:
Approximation algorithms
Estimation
Extended Kalman Filter
Land vehicles
Mobile Ground Vehicles
Odometry
Pose Estimation
Space-State
Trajectory
Velocity measurement
Wheels
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Nunez-Lopez, Juan D.
Lopez-Gonzalez, Alexandro
Ramirez-Neria, Mario
Tejada, Juan C.
description This paper delves into the initial phase of developing an autonomous navigation system for mobile platforms. It examines the impact of wheel velocity measurements on odometry estimation, considering the transition from a reliable and precise signal to noisy and less dependable information. The study investigates how this range of available data influences the accuracy of pose estimation for a ground vehicle. Additionally, the paper explores the incorporation of the Kalman Filter as a supplementary tool to enhance the input data quality, resulting in a consistent and reliable estimation of the ground vehicle's position and orientation. The findings presented herein contribute to the advancement of autonomous navigation systems, providing valuable insights into improving odometry estimation for mobile ground vehicles.
doi_str_mv 10.1109/CCAC58200.2023.10333445
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source IEEE Xplore All Conference Series
subjects Approximation algorithms
Estimation
Extended Kalman Filter
Land vehicles
Mobile Ground Vehicles
Odometry
Pose Estimation
Space-State
Trajectory
Velocity measurement
Wheels
title Enhancing Odometry Estimation for Mobile Ground Vehicles: Leveraging Wheel Velocity Measurements and the Kalman Filter
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