Building accurate maps is a key building block to enable reliable
localization, planning, and navigation of autonomous vehicles. We propose a
novel approach for building accurate maps of dynamic environments utilizing a
sequence of LiDAR scans. To this end, we propose encoding the 4D scene into a
novel spatio-temporal implicit neural map representation by fitting a
time-dependent truncated signed distance function to each point. Using our
representation, we extract the static map by filtering the dynamic parts. Our
neural representation is based on sparse feature grids, a globally shared
decoder, and time-dependent basis functions, which we jointly optimize in an
unsupervised fashion. To learn this representation from a sequence of LiDAR
scans, we design a simple yet efficient loss function to supervise the map
optimization in a piecewise way. We evaluate our approach on various scenes
containing moving objects in terms of the reconstruction quality of static maps
and the segmentation of dynamic point clouds. The experimental results
demonstrate that our method is capable of removing the dynamic part of the
input point clouds while reconstructing accurate and complete 3D maps,
outperforming several state-of-the-art methods. Codes are available at:
this https URL

基于 LiDAR 扫描序列，我们提出了一种构建动态环境准确地图的新方法，利用四维场景进行稀疏特征格、全局共享解码器和时变基函数的神经表示，用于滤除动态部分并重建准确完整的三维地图。

使用 4D 隐式神经表示在动态环境中进行的 3D LiDAR 地图构建

3D LiDAR Mapping in Dynamic Environments Using a 4D Implicit Neural  Representation

In automated driving, highly accurate maps are commonly used to support and
complement perception. These maps are costly to create and quickly become
outdated as the traffic world is permanently changing. In order to support or
replace the map of an automated system with detections from sensor data, a
perception module must be able to detect the map features. We propose a neural
network that follows the one shot philosophy of YOLO but is designed for
detection of 1D structures in images, such as lane boundaries.
We extend previous ideas by a midpoint based line representation and anchor
definitions. This representation can be used to describe lane borders,
markings, but also implicit features such as centerlines of lanes. The broad
applicability of the approach is shown with the detection performance on lane
centerlines, lane borders as well as the markings both on highways and in urban
areas.
Versatile lane boundaries are detected and can be inherently classified as
dashed or solid lines, curb, road boundaries, or implicit delimitation.

基于神经网络，通过一次性检测的方法，研究在自动驾驶中以图像为基础的 1D 结构检测，如车道边界。提出了一种基于中点和锚点定义的车道边界表示方法，能够检测车道中心线、车道边界以及高速公路和城市区域中的标志，实现了多功能检测和分类。