Related papers: Learning Position From Vehicle Vibration Using an Inertial Measurement Unit

Learning Position From Vehicle Vibration Using an Inertial Measurement Unit

URL: http://arxiv.org/abs/2303.03942v2
Date: Sat, 29 Jun 2024 09:47:09 GMT
Title: Learning Position From Vehicle Vibration Using an Inertial Measurement Unit
Authors: Barak Or, Nimrod Segol, Areej Eweida, Maxim Freydin,
Abstract summary: This paper presents a novel approach to vehicle positioning that operates without reliance on the global navigation satellite system (GNSS) Traditional approaches are vulnerable to interference in certain environments, rendering them unreliable in situations such as urban canyons, under flyovers, or in low reception areas. This study proposes a vehicle positioning method based on learning the road signature from accelerometer and gyroscope measurements obtained by an inertial measurement unit (IMU) sensor.
Score: 2.1213500139850012
License: http://creativecommons.org/licenses/by/4.0/
Abstract: This paper presents a novel approach to vehicle positioning that operates without reliance on the global navigation satellite system (GNSS). Traditional GNSS approaches are vulnerable to interference in certain environments, rendering them unreliable in situations such as urban canyons, under flyovers, or in low reception areas. This study proposes a vehicle positioning method based on learning the road signature from accelerometer and gyroscope measurements obtained by an inertial measurement unit (IMU) sensor. In our approach, the route is divided into segments, each with a distinct signature that the IMU can detect through the vibrations of a vehicle in response to subtle changes in the road surface. The study presents two different data-driven methods for learning the road segment from IMU measurements. One method is based on convolutional neural networks and the other on ensemble random forest applied to handcrafted features. Additionally, the authors present an algorithm to deduce the position of a vehicle in real-time using the learned road segment. The approach was applied in two positioning tasks: (i) a car along a 6[km] route in a dense urban area; (ii) an e-scooter on a 1[km] route that combined road and pavement surfaces. The mean error between the proposed method's position and the ground truth was approximately 50[m] for the car and 30[m] for the e-scooter. Compared to a solution based on time integration of the IMU measurements, the proposed approach has a mean error of more than 5 times better for e-scooters and 20 times better for cars.

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