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

TL;DR

WiCHINS addresses pure inertial navigation for wheeled robots in GNSS-denied environments by fusing wheel-mounted IMUs with a chassis-mounted IMU via a three-stage EKF pipeline. The framework consists of $WheelEKF$, $OriEKF$, and $PosEKF$, which separate wheel-kinematics, body orientation, and forward motion estimation to reduce drift. Evaluated on a real-world dataset with five IMUs, WiCHINS achieves an average 3D position error of $11.4$ m (about $2.4\%$ of traveled distance), and results indicate that using two wheel IMUs plus one chassis IMU suffices to bridge much of the pure-inertial performance gap. The approach offers a robust, self-contained navigation alternative in GNSS-denied scenarios, with limitations including yaw drift without magnetometer updates and no-skid assumptions in the inverse-kinematics-based PosEKF.

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.

Figures (7)

• Figure 1: An illustration of a Skoda Roomster with reference frames: the wheel has the w frame, the car roof has the body frame (lowercase), and the car roof also shows the navigation frame (uppercase).
• Figure 2: Block diagram our WiCHINS framework. Green blocks represent sources of information (sensors or constants), orange blocks are operations, blue blocks are EKF-based estimators, with state input measurements denoted as "predict", and output measurements as "update". Dashed lines represent signals passed to EKF as parameters.
• Figure 3: Skoda Roomster equipped with wheel-mounted IMUs and a GNSS-RTK on the roof ourdata26
• Figure 4: The GT trajectory and the 2WiCHINS and ODO trajectory estimates. Left: Trajectory 1. Right: Trajectory 2.
• Figure 5: Estimated angular velocity around vertical axis in the body frame: comparison of our approach (2WiCHINS), with the wheel-based odometry (ODO). Left: Trajectory 1. Right: Trajectory 2.