Depth perception is considered an invaluable source of information in the context of 3D mapping and various robotics applications. However, point cloud maps acquired using consumer-level light detection and ranging sensors (lidars) still suffer from bias related to local surface properties such as measuring beam-to-surface incidence angle, distance, texture, reflectance, or illumination conditions. This fact has recently motivated researchers to exploit traditional filters, as well as the deep learning paradigm, in order to suppress the aforementioned depth sensors error while preserving geometric and map consistency details. Despite the effort, depth correction of lidar measurements is still an open challenge mainly due to the lack of clean 3D data that could be used as ground truth. In this paper, we introduce two novel point cloud map consistency losses, which facilitate self-supervised learning on real data of lidar depth correction models. Specifically, the models exploit multiple point cloud measurements of the same scene from different view-points in order to learn to reduce the bias based on the constructed map consistency signal. Complementary to the removal of the bias from the measurements, we demonstrate that the depth correction models help to reduce localization drift. Additionally, we release a data set that contains point cloud data captured in an indoor corridor environment with precise localization and ground truth mapping information.

翻译：深度感知被认为是三维地图构建及各类机器人应用中极具价值的信息来源。然而，使用消费级激光雷达传感器获取的点云地图仍存在与局部表面特性相关的偏差，例如测量光束与表面的入射角、距离、纹理、反射率或光照条件等因素。这促使研究者近期尝试利用传统滤波器及深度学习范式，在抑制上述深度传感器误差的同时保留几何与地图一致性细节。尽管已有诸多努力，激光雷达测量的深度校正仍是一个待解决的挑战，主要原因是缺乏可作为真值的洁净三维数据。本文提出了两种新颖的点云地图一致性损失函数，可基于真实数据实现激光雷达深度校正模型的自监督学习。具体而言，模型通过利用同一场景在不同视角下的多次点云测量，基于构建的地图一致性信号学习降低测量偏差。除消除测量偏差外，我们还证明深度校正模型有助于减少定位漂移。此外，我们发布了一个数据集，包含在室内走廊环境中采集的点云数据及其精确位姿和真值地图信息。