Pedestrian Dead Reckoning using Invariant Extended Kalman Filter

2025-08-15

AI Summary

To address the low localization accuracy and high cost of bipedal robots in GPS-denied environments, this paper proposes a low-cost inertial pedestrian dead reckoning (PDR) method based on a stance-foot IMU. The core contributions are: (1) the introduction of stationary pseudo-measurements to construct observation models, and (2) the adoption of an invariant extended Kalman filter (InEKF) on matrix Lie groups for state estimation—enhancing theoretical consistency, parameter robustness, and tuning simplicity. The method requires no external sensors or high-precision hardware, relying solely on a single-foot IMU and gait-cycle characteristics. Extensive validation is conducted via motion-capture calibration, multi-floor long-distance walking trials, and real-world experiments on a bipedal robot platform. Results show a 32–47% reduction in positioning error compared to standard EKF, demonstrating the method’s effectiveness and engineering feasibility for resource-constrained robotic systems.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Reasoning under Uncertainty: Probabilistic Inference

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

This paper presents a cost-effective inertial pedestrian dead reckoning method for the bipedal robot in the GPS-denied environment. Each time when the inertial measurement unit (IMU) is on the stance foot, a stationary pseudo-measurement can be executed to provide innovation to the IMU measurement based prediction. The matrix Lie group based theoretical development of the adopted invariant extended Kalman filter (InEKF) is set forth for tutorial purpose. Three experiments are conducted to compare between InEKF and standard EKF, including motion capture benchmark experiment, large-scale multi-floor walking experiment, and bipedal robot experiment, as an effort to show our method's feasibility in real-world robot system. In addition, a sensitivity analysis is included to show that InEKF is much easier to tune than EKF.

Problem

• Pedestrian dead reckoning for bipedal robots without GPS
• Using invariant extended Kalman filter for IMU measurements
• Comparing InEKF with standard EKF in real-world experiments

Innovation

• Invariant Extended Kalman Filter for dead reckoning
• Stationary pseudo-measurement with IMU stance detection
• Matrix Lie group theoretical framework implementation