High-precision vehicle localization with commercial setups is a crucial
technique for high-level autonomous driving tasks. Localization with a
monocular camera in LiDAR map is a newly emerged approach that achieves
promising balance between cost and accuracy, but estimating pose by finding
correspondences between such cross-modal sensor data is challenging, thereby
damaging the localization accuracy. In this paper, we address the problem by
proposing a novel Transformer-based neural network to register 2D images into
3D LiDAR map in an end-to-end manner. Poses are implicitly represented as
high-dimensional feature vectors called pose queries and can be iteratively
updated by interacting with the retrieved relevant information from cross-model
features using attention mechanism in a proposed POse Estimator Transformer
(POET) module. Moreover, we apply a multiple hypotheses aggregation method that
estimates the final poses by performing parallel optimization on multiple
randomly initialized pose queries to reduce the network uncertainty.
Comprehensive analysis and experimental results on public benchmark conclude
that the proposed image-to-LiDAR map localization network could achieve
state-of-the-art performances in challenging cross-modal localization tasks.

本研究提出一种新型的基于 Transformer 的神经网络，可以在 end-to-end 的情况下将 2D 图像注册到 3D LiDAR 地图中进行车辆定位，达到了一定的准确度和实用性。

用 Transformer 实现姿态作为查询：图像到 LiDAR 地图的定位

Poses as Queries: Image-to-LiDAR Map Localization with Transformers

Predicting the future location of vehicles is essential for safety-critical
applications such as advanced driver assistance systems (ADAS) and autonomous
driving. This paper introduces a novel approach to simultaneously predict both
the location and scale of target vehicles in the first-person (egocentric) view
of an ego-vehicle. We present a multi-stream recurrent neural network (RNN)
encoder-decoder model that separately captures both object location and scale
and pixel-level observations for future vehicle localization. We show that
incorporating dense optical flow improves prediction results significantly
since it captures information about motion as well as appearance change. We
also find that explicitly modeling future motion of the ego-vehicle improves
the prediction accuracy, which could be especially beneficial in intelligent
and automated vehicles that have motion planning capability. To evaluate the
performance of our approach, we present a new dataset of first-person videos
collected from a variety of scenarios at road intersections, which are
particularly challenging moments for prediction because vehicle trajectories
are diverse and dynamic.

本文介绍了一种新颖的方法来同时预测自动驾驶中车辆的位置和规模，这主要是通过使用多流递归神经网络编码器 - 解码器模型来实现，在该模型中，密集的光流加入了捕捉运动和外观变化信息的能力。此外，本文还发现明确建模车辆的未来运动可以提高预测准确性。

基于自我中心视角的未来车辆本体定位技术研究

Egocentric Vision-based Future Vehicle Localization for Intelligent  Driving Assistance Systems

We propose a method for accurately localizing ground vehicles with the aid of
satellite imagery. Our approach takes a ground image as input, and outputs the
location from which it was taken on a georeferenced satellite image. We perform
visual localization by estimating the co-occurrence probabilities between the
ground and satellite images based on a ground-satellite feature dictionary. The
method is able to estimate likelihoods over arbitrary locations without the
need for a dense ground image database. We present a ranking-loss based
algorithm that learns location-discriminative feature projection matrices that
result in further improvements in accuracy. We evaluate our method on the
Malaga and KITTI public datasets and demonstrate significant improvements over
a baseline that performs exhaustive search.

该研究提出了一种利用卫星图像对地面车辆进行精确定位的方法，其采用基于地面 - 卫星特征字典的视觉定位方法，可以在不需要密集的地面图像数据库的情况下估计任意位置的可能性，并通过学习定位判别特征投影矩阵进一步提高精确度，在公共数据集上得到了显著的改进。