MoRPI: Mobile Robot Pure Inertial Navigation

TL;DR

This paper introduces MoRPI, a novel pure inertial navigation method for mobile robots that uses periodic motion and empirical formulas to reduce position drift, validated through extensive field experiments.

Contribution

The paper presents the MoRPI framework, a new inertial navigation approach that improves accuracy by leveraging periodic motion and empirical distance estimation formulas.

Findings

MoRPI reduces position error compared to classical inertial solutions.

Field experiments with 143 trajectories validate the approach.

Both accelerometer and gyroscope-based methods are effective.

Abstract

Mobile robots are used in industrial, leisure, and military applications. In some situations, a robot navigation solution relies only on inertial sensors and as a consequence, the navigation solution drifts in time. In this paper, we propose the MoRPI framework, a mobile robot pure inertial approach. Instead of travelling in a straight line trajectory, the robot moves in a periodic motion trajectory to enable peak-to-peak estimation. In this manner, instead of performing three integrations to calculate the robot position in a classical inertial solution, an empirical formula is used to estimate the travelled distance. Two types of MoRPI approaches are suggested, where one is based on both accelerometer and gyroscope readings while the other is only on gyroscopes. Closed form analytical solutions are derived to show that MoRPI produces lower position error compared to the classical pure inertial solution. In addition, to evaluate the proposed approach, field experiments were made with a mobile robot equipped with two types of inertial sensors. In total, 143 trajectories with a time duration of 75 minutes were collected and evaluated. The results show the benefits of using our approach. To facilitate further development of the proposed approach, both dataset and code are publicly available at https://github.com/ansfl/MoRPI.