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.

翻译：本文提出一种新颖方法，通过融合气压传感器的高度约束来提升迭代最近点（ICP）算法的精度。虽然ICP在移动机器人同步定位与建图（SLAM）领域被广泛应用，但其易受漂移影响，在竖井等约束不足的环境中尤为明显。为解决该问题，我们提出利用高度计测量值增强ICP算法，从而可靠地约束沿重力矢量的漂移。为论证高度测量在SLAM中的潜力，我们系统分析了多种压力传感器的校准流程与噪声敏感度，将测量精度提升至厘米级。基于此精度优势，我们提出一种新型ICP公式，可沿重力矢量融合高度测量值，从而将优化问题简化为三自由度。实际部署实验表明，在非平面环境中，相较于现有先进方法，本方法可减少84%的垂直漂移，并显著提升整体定位精度。