Mobile ground robots require perceiving and understanding their surrounding support surface to move around autonomously and safely. The support surface is commonly estimated based on exteroceptive depth measurements, e.g., from LiDARs. However, the measured depth fails to align with the true support surface in the presence of high grass or other penetrable vegetation. In this work, we present the Semantic Pointcloud Filter (SPF), a Convolutional Neural Network (CNN) that learns to adjust LiDAR measurements to align with the underlying support surface. The SPF is trained in a semi-self-supervised manner and takes as an input a LiDAR pointcloud and RGB image. The network predicts a binary segmentation mask that identifies the specific points requiring adjustment, along with estimating their corresponding depth values. To train the segmentation task, 300 distinct images are manually labeled into rigid and non-rigid terrain. The depth estimation task is trained in a self-supervised manner by utilizing the future footholds of the robot to estimate the support surface based on a Gaussian process. Our method can correctly adjust the support surface prior to interacting with the terrain and is extensively tested on the quadruped robot ANYmal. We show the qualitative benefits of SPF in natural environments for elevation mapping and traversability estimation compared to using raw sensor measurements and existing smoothing methods. Quantitative analysis is performed in various natural environments, and an improvement by 48% RMSE is achieved within a meadow terrain.

翻译：移动地面机器人需要感知并理解其周围的支撑地面，以实现自主且安全地移动。支撑地面通常基于外部深度测量（例如来自LiDAR）进行估计。然而，在存在高草或其他可穿透植被的情况下，测量的深度无法与真实支撑表面对齐。在这项工作中，我们提出了语义点云滤波器（SPF），这是一种卷积神经网络（CNN），它学习调整LiDAR测量值以与下方支撑表面对齐。SPF以半自监督方式进行训练，并将LiDAR点云和RGB图像作为输入。网络预测一个二值分割掩码，用于识别需要调整的特定点，同时估计其对应的深度值。为了训练分割任务，我们手动将300张不同的图像标注为刚性和非刚性地形。深度估计任务以自监督方式训练，通过利用机器人未来的落脚点，基于高斯过程估计支撑表面。我们的方法能够在地形交互之前正确调整支撑表面，并在四足机器人ANYmal上进行了广泛测试。我们展示了SPF在自然环境中相较于使用原始传感器测量和现有平滑方法，在地图高程构建和可通行性估计方面的定性优势。在多种自然环境中进行了定量分析，并在草地地形上实现了48%的RMSE改善。