This paper is about 3D pose estimation on LiDAR scans with extremely minimal
storage requirements to enable scalable mapping and localisation. We achieve
this by clustering all points of segmented scans into semantic objects and
representing them only with their respective centroid and semantic class. In
this way, each LiDAR scan is reduced to a compact collection of four-number
vectors. This abstracts away important structural information from the scenes,
which is crucial for traditional registration approaches. To mitigate this, we
introduce an object-matching network based on self- and cross-correlation that
captures geometric and semantic relationships between entities. The respective
matches allow us to recover the relative transformation between scans through
weighted Singular Value Decomposition (SVD) and RANdom SAmple Consensus
(RANSAC). We demonstrate that such representation is sufficient for metric
localisation by registering point clouds taken under different viewpoints on
the KITTI dataset, and at different periods of time localising between KITTI
and KITTI-360. We achieve accurate metric estimates comparable with
state-of-the-art methods with almost half the representation size, specifically
1.33 kB on average.

使用极其少的存储要求进行 LiDAR 扫描的 3D 姿态估计以实现可扩展的映射和定位，通过将分割扫描的所有点聚类成语义对象并仅用其质心和语义类别表示，以此方式将每个 LiDAR 扫描简化为一个紧凑的四元向量集合，并通过基于自相关和互相关的对象匹配网络捕捉实体之间的几何和语义关系，通过加权奇异值分解（SVD）和随机采样一致性（RANSAC）恢复扫描之间的相对变换，通过在 KITTI 数据集上不同视点拍摄的点云和不同时间段的 KITTI 和 KITTI-360 之间进行本地化来演示这种表示对度量本地化是足够准确的，具有几乎一半的表示大小，具体为平均 1.33kB。

紧凑的物体中心 LiDAR 位姿估计对于大尺度户外定位

That's My Point: Compact Object-centric LiDAR Pose Estimation for  Large-scale Outdoor Localisation

This paper presents a self-supervised framework for learning to detect robust
keypoints for odometry estimation and metric localisation in radar. By
embedding a differentiable point-based motion estimator inside our
architecture, we learn keypoint locations, scores and descriptors from
localisation error alone. This approach avoids imposing any assumption on what
makes a robust keypoint and crucially allows them to be optimised for our
application. Furthermore the architecture is sensor agnostic and can be applied
to most modalities. We run experiments on 280km of real world driving from the
Oxford Radar RobotCar Dataset and improve on the state-of-the-art in
point-based radar odometry, reducing errors by up to 45% whilst running an
order of magnitude faster, simultaneously solving metric loop closures.
Combining these outputs, we provide a framework capable of full mapping and
localisation with radar in urban environments.

本文提出了一种用于雷达中自学习检测鲁棒关键点从而进行里程计估计和度量本地化的自监督框架。通过在我们的架构中嵌入可微分的基于点的运动估计器，我们仅通过本地化误差学习关键点的位置、分数和描述符，该方法避免了对什么是鲁棒关键点的任何假设，并且能够应用于大多数模态。在牛津雷达机器人车数据集的 280 公里真实驾驶实验中，我们改善了基于点的雷达里程表的最新研究成果，将误差降低了 45％，同时速度提高了一个数量级，并可同时解决度量环路闭合的问题。结合这些输出，我们提供了一个能够在城市环境下使用雷达进行完整地图绘制和本地化的框架。

雷达下的关键点鲁棒性预测学习：在里程计估计和度量定位中的应用

Under the Radar: Learning to Predict Robust Keypoints for Odometry  Estimation and Metric Localisation in Radar

We present a method of improving visual place recognition and metric
localisation under very strong appear- ance change. We learn an invertable
generator that can trans- form the conditions of images, e.g. from day to
night, summer to winter etc. This image transforming filter is explicitly
designed to aid and abet feature-matching using a new loss based on SURF
detector and dense descriptor maps. A network is trained to output synthetic
images optimised for feature matching given only an input RGB image, and these
generated images are used to localize the robot against a previously built map
using traditional sparse matching approaches. We benchmark our results using
multiple traversals of the Oxford RobotCar Dataset over a year-long period,
using one traversal as a map and the other to localise. We show that this
method significantly improves place recognition and localisation under changing
and adverse conditions, while reducing the number of mapping runs needed to
successfully achieve reliable localisation.

本文提出了一种通过学习可逆的生成器将图像从一种条件转换到另一种的方法，以此改善在外观强烈变化的情况下的视觉场所识别和度量本地化效果，并基于 SURF 检测器和密集描述符映射的新损失进行特征匹配。数据测试表明，这种方法能够在减少映射运行次数的同时，在多种环境下显著提高地点识别和定位的效果。