Recent advancements in Bird's Eye View (BEV) fusion for map construction have
demonstrated remarkable mapping of urban environments. However, their deep and
bulky architecture incurs substantial amounts of backpropagation memory and
computing latency. Consequently, the problem poses an unavoidable bottleneck in
constructing high-resolution (HR) BEV maps, as their large-sized features cause
significant increases in costs including GPU memory consumption and computing
latency, named diverging training costs issue. Affected by the problem, most
existing methods adopt low-resolution (LR) BEV and struggle to estimate the
precise locations of urban scene components like road lanes, and sidewalks. As
the imprecision leads to risky self-driving, the diverging training costs issue
has to be resolved. In this paper, we address the issue with our novel Trumpet
Neural Network (TNN) mechanism. The framework utilizes LR BEV space and outputs
an up-sampled semantic BEV map to create a memory-efficient pipeline. To this
end, we introduce Local Restoration of BEV representation. Specifically, the
up-sampled BEV representation has severely aliased, blocky signals, and thick
semantic labels. Our proposed Local Restoration restores the signals and thins
(or narrows down) the width of the labels. Our extensive experiments show that
the TNN mechanism provides a plug-and-play memory-efficient pipeline, thereby
enabling the effective estimation of real-sized (or precise) semantic labels
for BEV map construction.

本文通过 Trumpet 神经网络解决高分辨率鸟瞰地图构建中存在的训练成本问题，利用低分辨率空间生成内存高效的管道，从而有效估计真实尺寸的语义标签用于鸟瞰地图构建。

解决不同训练成本的问题：局部修复用于精确鸟瞰地图构建

Addressing Diverging Training Costs using Local Restoration for Precise  Bird's Eye View Map Construction

Building on progress in feature representations for image retrieval,
image-based localization has seen a surge of research interest. Image-based
localization has the advantage of being inexpensive and efficient, often
avoiding the use of 3D metric maps altogether. That said, the need to maintain
a large number of reference images as an effective support of localization in a
scene, nonetheless calls for them to be organized in a map structure of some
kind.
The problem of localization often arises as part of a navigation process. We
are, therefore, interested in summarizing the reference images as a set of
landmarks, which meet the requirements for image-based navigation. A
contribution of this paper is to formulate such a set of requirements for the
two sub-tasks involved: map construction and self-localization. These
requirements are then exploited for compact map representation and accurate
self-localization, using the framework of a network flow problem. During this
process, we formulate the map construction and self-localization problems as
convex quadratic and second-order cone programs, respectively. We evaluate our
methods on publicly available indoor and outdoor datasets, where they
outperform existing methods significantly.

本文提出一种基于图像的定位方法，针对图像检索领域的发展，将大量的参考图像整理为一种可以满足导航系统要求的地标集合，并通过网络流问题的框架为紧凑的地图表示和准确的自我定位提供了可行方案。

使用视觉特征和地图进行基于图像的导航的映射、定位和路径规划

Mapping, Localization and Path Planning for Image-based Navigation using  Visual Features and Map

Map construction methods automatically produce and/or update road network
datasets using vehicle tracking data. Enabled by the ubiquitous generation of
georeferenced tracking data, there has been a recent surge in map construction
algorithms coming from different computer science domains. A cross-comparison
of the various algorithms is still very rare, since (i) algorithms and
constructed maps are generally not publicly available and (ii) there is no
standard approach to assess the result quality, given the lack of benchmark
data and quantitative evaluation methods. This work represents a first
comprehensive attempt to benchmark map construction algorithms. We provide an
evaluation and comparison of seven algorithms using four datasets and four
different evaluation measures. In addition to this comprehensive comparison, we
make our datasets, source code of map construction algorithms and evaluation
measures publicly available on mapconstruction.org. This site has been
established as a repository for map con- struction data and algorithms and we
invite other researchers to contribute by uploading code and benchmark data
supporting their contributions to map construction algorithms.

本文提出了一种基于车辆跟踪数据的综合比较七种地图构建算法的方法，并使用四种不同的数据集和四种不同的评估指标进行了评估和比较。