Pure Inertial Navigation in Challenging Environments with Wheeled and Chassis Mounted Inertial Sensors

TL;DR

This paper introduces WiCHINS, a novel inertial navigation system combining wheel-mounted and chassis-mounted sensors with a three-stage Kalman filter framework, enabling accurate navigation in environments where GNSS signals are unreliable.

Contribution

The paper presents a new multi-sensor inertial navigation approach with a specialized three-stage Kalman filter framework, improving accuracy over existing methods in challenging conditions.

Findings

Achieved an average position error of 11.4 meters over 228.6 minutes.

Demonstrated 2.4% error relative to traveled distance.

Outperformed four baseline inertial navigation methods.

Abstract

Autonomous vehicles and wheeled robots are widely used in many applications in both indoor and outdoor settings. In practical situations with limited GNSS signals or degraded lighting conditions, the navigation solution may rely only on inertial sensors and as result drift in time due to errors in the inertial measurement. In this work, we propose WiCHINS, a wheeled and chassis inertial navigation system by combining wheel-mounted-inertial sensors with a chassis-mounted inertial sensor for accurate pure inertial navigation. To that end, we derive a three-stage framework, each with a dedicated extended Kalman filter. This framework utilizes the benefits of each location (wheel/body) during the estimation process. To evaluate our proposed approach, we employed a dataset with five inertial measurement units with a total recording time of 228.6 minutes. We compare our approach with four other inertial baselines and demonstrate an average position error of 11.4m, which is $2.4\%$ of the average traveled distance, using two wheels and one body inertial measurement units. As a consequence, our proposed method enables robust navigation in challenging environments and helps bridge the pure-inertial performance gap.