Under Pressure: Altimeter-Aided ICP for 3D Maps Consistency

TL;DR

This paper introduces an enhanced ICP algorithm that incorporates altimeter data to significantly reduce vertical drift and improve 3D map accuracy in SLAM, especially in environments with limited constraints.

Contribution

The paper presents a novel ICP formulation integrating altitude measurements from pressure sensors, enabling more accurate 3D mapping and localization in challenging environments.

Findings

Reduces vertical drift by 84% in real-world tests.

Achieves centimeter-level altitude measurement accuracy.

Improves overall localization accuracy over existing methods.

Abstract

We propose a novel method to enhance the accuracy of the Iterative Closest Point (ICP) algorithm by integrating altitude constraints from a barometric pressure sensor. While ICP is widely used in mobile robotics for Simultaneous Localization and Mapping ( SLAM ), it is susceptible to drift, especially in underconstrained environments such as vertical shafts. To address this issue, we propose to augment ICP with altimeter measurements, reliably constraining drifts along the gravity vector. To demonstrate the potential of altimetry in SLAM , we offer an analysis of calibration procedures and noise sensitivity of various pressure sensors, improving measurements to centimeter-level accuracy. Leveraging this accuracy, we propose a novel ICP formulation that integrates altitude measurements along the gravity vector, thus simplifying the optimization problem to 3-Degree Of Freedom (DOF). Experimental results from real-world deployments demonstrate that our method reduces vertical drift by 84% and improves overall localization accuracy compared to state-of-the-art methods in non-planar environments.