OdoNet: Untethered Speed Aiding for Vehicle Navigation Without Hardware Wheeled Odometer

TL;DR

OdoNet is a CNN-based pseudo-odometer that uses a single IMU to improve vehicle navigation accuracy in GNSS-challenged environments, offering an untethered alternative to hardware odometers with robust performance.

Contribution

This paper introduces OdoNet, a novel deep learning model that estimates vehicle speed from IMU data, eliminating the need for hardware odometers in navigation systems.

Findings

OdoNet reduces positioning error by around 68% compared to non-holonomic constraints.

IMU biases and mounting angles significantly affect OdoNet, but data-cleaning mitigates this impact.

OdoNet achieves comparable accuracy to hardware odometers in vehicle navigation.

Abstract

Odometer has been proven to significantly improve the accuracy of the Global Navigation Satellite System / Inertial Navigation System (GNSS/INS) integrated vehicle navigation in GNSS-challenged environments. However, the odometer is inaccessible in many applications, especially for aftermarket devices. To apply forward speed aiding without hardware wheeled odometer, we propose OdoNet, an untethered one-dimensional Convolution Neural Network (CNN)-based pseudo-odometer model learning from a single Inertial Measurement Unit (IMU), which can act as an alternative to the wheeled odometer. Dedicated experiments have been conducted to verify the feasibility and robustness of the OdoNet. The results indicate that the IMU individuality, the vehicle loads, and the road conditions have little impact on the robustness and precision of the OdoNet, while the IMU biases and the mounting angles may notably ruin the OdoNet. Thus, a data-cleaning procedure is added to effectively mitigate the impacts of the IMU biases and the mounting angles. Compared to the process using only non-holonomic constraint (NHC), after employing the pseudo-odometer, the positioning error is reduced by around 68%, while the percentage is around 74% for the hardware wheeled odometer. In conclusion, the proposed OdoNet can be employed as an untethered pseudo-odometer for vehicle navigation, which can efficiently improve the accuracy and reliability of the positioning in GNSS-denied environments.