This paper describes the hardware design and flight demonstration of a small
quadrotor with imaging sensors for urban mapping, hazard avoidance, and target
tracking research. The vehicle is equipped with five cameras, including two
pairs of fisheye stereo cameras that enable a nearly omnidirectional view and a
two-axis gimbaled camera. An onboard NVIDIA Jetson Orin Nano computer running
the Robot Operating System software is used for data collection. An autonomous
tracking behavior was implemented to coordinate the motion of the quadrotor and
gimbaled camera to track a moving GPS coordinate. The data collection system
was demonstrated through a flight test that tracked a moving GPS-tagged vehicle
through a series of roads and parking lots. A map of the environment was
reconstructed from the collected images using the Direct Sparse Odometry (DSO)
algorithm. The performance of the quadrotor was also characterized by acoustic
noise, communication range, battery voltage in hover, and maximum speed tests.

本文描述了一种使用成像传感器进行城市建模、危险回避和目标追踪研究的小型四旋翼飞行器的硬件设计和飞行演示。该飞行器配备了五个摄像头，包括两对鱼眼立体摄像头，可以实现几乎全方位视图，并配备了一个两轴云台摄像头。飞行器使用一台运行机器人操作系统软件的 NVIDIA Jetson Orin Nano 计算机用于数据收集。实施了一种自主跟踪行为，以协调四旋翼飞行器和云台摄像头的运动，以跟踪移动的 GPS 坐标。通过对一辆标记了 GPS 的机动车在一系列道路和停车场上的跟踪飞行测试演示了数据收集系统。使用直接稀疏测距（DSO）算法从收集的图像重构了环境的地图。四旋翼飞行器的性能还通过声音、通信范围、悬停时的电池电压和最大速度测试进行了表征。

城市地图绘制与目标跟踪研究用四旋翼飞行器的设计与飞行演示

Design and Flight Demonstration of a Quadrotor for Urban Mapping and  Target Tracking Research

Point clouds are widely regarded as one of the best dataset types for urban
mapping purposes. Hence, point cloud datasets are commonly investigated as
benchmark types for various urban interpretation methods. Yet, few researchers
have addressed the use of point cloud benchmarks for fa\c{c}ade segmentation.
Robust fa\c{c}ade segmentation is becoming a key factor in various applications
ranging from simulating autonomous driving functions to preserving cultural
heritage. In this work, we present a method of enriching existing point cloud
datasets with fa\c{c}ade-related classes that have been designed to facilitate
fa\c{c}ade segmentation testing. We propose how to efficiently extend existing
datasets and comprehensively assess their potential for fa\c{c}ade
segmentation. We use the method to create the TUM-FA\c{C}ADE dataset, which
extends the capabilities of TUM-MLS-2016. Not only can TUM-FA\c{C}ADE
facilitate the development of point-cloud-based fa\c{c}ade segmentation tasks,
but our procedure can also be applied to enrich further datasets.

该研究提出了一种用于扩展点云数据集的方法，以便更好地评估其在建筑物立面分割中的应用潜力，并创建了 TUM-FAÇADE 数据集用于该目的。

TUM-FAÇADE: 为建筑物立面分割检验指标进行评估和拓展

TUM-FAÇADE: Reviewing and enriching point cloud benchmarks for  façade segmentation

In the era of autonomous driving, urban mapping represents a core step to let
vehicles interact with the urban context. Successful mapping algorithms have
been proposed in the last decade building the map leveraging on data from a
single sensor. The focus of the system presented in this paper is twofold: the
joint estimation of a 3D map from lidar data and images, based on a 3D mesh,
and its texturing. Indeed, even if most surveying vehicles for mapping are
endowed by cameras and lidar, existing mapping algorithms usually rely on
either images or lidar data; moreover both image-based and lidar-based systems
often represent the map as a point cloud, while a continuous textured mesh
representation would be useful for visualization and navigation purposes. In
the proposed framework, we join the accuracy of the 3D lidar data, and the
dense information and appearance carried by the images, in estimating a
visibility consistent map upon the lidar measurements, and refining it
photometrically through the acquired images. We evaluate the proposed framework
against the KITTI dataset and we show the performance improvement with respect
to two state of the art urban mapping algorithms, and two widely used surface
reconstruction algorithms in Computer Graphics.

在自动驾驶时代，本文介绍了一种基于图像和激光雷达数据的三维城市地图构建算法及其纹理映射实现的方法，并在 KITTI 数据集上进行了表现评估。