用传感器视野形状优化高斯过程潜空间，提升占用地图构建效率
Towards Efficient Occupancy Mapping via Gaussian Process Latent Field Shaping
- 直接操作高斯过程潜变量，利用传感器视野形状融合自由空间先验
- 在模拟环境中实现与现有方法相当的重建精度，计算更高效
- 适合对实时性要求高的移动机器人占用地图建模任务
占用地图是移动机器人的重要支撑技术。传统离散网格表示已演进为连续表示，可预测任意位置的占据状态并建模邻近区域的占据相关性。高斯过程（GP）方法将此视为二分类问题，通过逻辑函数对GP潜场进行变换以获得输出类别，但未直接操作潜场。本文提出直接调整潜场形状，利用传感器视场形状有效融合自由空间先验信息。关键区别在于重新定义分类问题：区分自由与未知空间，占据区域为自由到未知的过渡边界（无限薄）。在模拟环境中验证表明，该方法具有一致性且重建精度具有竞争力。
原文摘要 · Abstract (English)
Occupancy mapping has been a key enabler of mobile robotics. Originally based on a discrete grid representation, occupancy mapping has evolved towards continuous representations that can predict the occupancy status at any location and account for occupancy correlations between neighbouring areas. Gaussian Process (GP) approaches treat this task as a binary classification problem using both observations of occupied and free space. Conceptually, a GP latent field is passed through a logistic function to obtain the output class without actually manipulating the GP latent field. In this work, we propose to act directly on the latent function to efficiently integrate free space information as a prior based on the shape of the sensor's field-of-view. A major difference with existing methods is the change in the classification problem, as we distinguish between free and unknown space. The `occupied' area is the infinitesimally thin location where the class transitions from free to unknown. We demonstrate in simulated environments that our approach is sound and leads to competitive reconstruction accuracy.
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