# Axes Mapping and Sensor Fusion for Attitude-Unconstrained Pedestrian Dead Reckoning

## TL;DR

This paper introduces a new pedestrian dead reckoning system that improves indoor navigation accuracy by fusing sensor data and adapting to smartphone placement.

## Contribution

A novel HEAT-MAP algorithm dynamically adjusts sensor axes for attitude-unconstrained pedestrian navigation.

## Key findings

- The proposed step counting algorithm fuses IMU data across three degrees of freedom for different body placements.
- The HEAT-MAP algorithm reduces heading drift by dynamically adjusting sensor axes based on smartphone orientation.
- Integration with adaptive Wi-Fi fingerprinting reduces cumulative errors and improves trajectory accuracy.

## Abstract

Localization and navigation techniques have become fundamental for modern lives, while achieving accurate results indoors still remains a significant challenge. The widespread adoption of smart devices, and especially smartphones, has increased the need for accurate and robust pedestrian dead reckoning systems that operate in infrastructure-less environments. Pedestrian dead reckoning’s primary challenge is maintaining accuracy despite varying smartphone placements (attitudes) and the noisy, low-cost inertial measurements units. In this work, a comprehensive pedestrian dead reckoning framework is presented that integrates advanced step counting and heading estimation techniques. For step detection and counting, we propose a robust step counting algorithm that utilizes the optimum fusion of the raw IMU readings, i.e., accelerometer, linear accelerometer, gyroscope, and magnetometer readings, each broken down into three degrees of freedom for different body placements and walking speeds. Furthermore, to address the critical issue of heading estimation, we propose the heading estimation axis mapping (HEAT-MAP) algorithm, which dynamically adjusts the sensor axes in response to the smartphone’s orientation, ensuring a consistent coordinate frame and reducing heading drift. Moreover, to eliminate cumulative pedestrian dead reckoning errors, the system incorporates an adaptive weighted fusion mechanism with Wi-Fi fingerprinting. Experimental results demonstrate that this integrated system significantly improves the overall trajectory accuracy, providing a high-precision, attitude-unconstrained solution for real-time indoor pedestrian navigation.