用气压计约束提升ICP在垂直空间的定位精度，减少84%竖直漂移。
Under Pressure: Altimeter-Aided ICP for 3D Maps Consistency
- 融合气压计高度数据，将ICP优化降至3自由度。
- 实测在非平面环境减少84%竖直漂移，定位更准。
- 适合垂直结构多的室内/井道导航，如机器人巡检。
我们提出一种新方法，通过整合气压计提供的高度约束，提升迭代最近点（ICP）算法的精度。尽管ICP广泛用于移动机器人同步定位与建图（SLAM），但在垂直通道等欠约束环境下易产生漂移。为此，我们引入气压计测量值，可靠地约束重力方向上的漂移。我们分析了多种压力传感器的校准流程与噪声敏感性，将测量精度提升至厘米级。基于此，提出一种新型ICP公式，沿重力方向融合高度信息，使优化问题简化为3自由度。真实场景实验表明，该方法在非平面环境中使竖直漂移降低84%，整体定位精度优于现有先进方法。
原文摘要 · Abstract (English)
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.
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