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Intelligent Robots: Localization, Mapping, and NavigationConstraint Satisfaction and Optimization: Constraint OptimizationSearch and Optimization: Local Search
Under Pressure: Altimeter-Aided ICP for 3D Maps Consistency
🤖 AI Summary
In vertically unconstrained environments such as shafts, ICP-SLAM suffers from severe drift along the gravity-aligned z-axis. To address this, we propose a tightly coupled constraint optimization method integrating a high-precision barometric altimeter. Our approach innovatively incorporates centimeter-level barometric height measurements as a strong vertical constraint, reducing the 6-DOF pose estimation problem to 3-DOF and formulating a gravity-aligned constrained ICP model. We further perform noise modeling and online calibration of the barometric sensor. Experiments in real shaft environments demonstrate an 84% reduction in vertical drift and significantly improved 3D map consistency. Moreover, our method achieves superior localization accuracy compared to state-of-the-art SLAM systems—including ORB-SLAM3 and LOAM—in unstructured settings. This work presents the first tightly coupled geometric optimization framework integrating barometric altimetry with ICP, establishing a robust, low-cost paradigm for vertical localization in confined-space robotic SLAM.
Technology Category
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Enhance ICP algorithm accuracy using altimeter data
Reduce vertical drift in SLAM for non-planar environments
Improve localization accuracy with centimeter-level altimeter precision
Innovation
Methods, ideas, or system contributions that make the work stand out.
Integrates altimeter data with ICP algorithm
Reduces vertical drift by 84%
Simplifies optimization to 3-DOF
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