Loop closing and relocalization are crucial techniques to establish reliable
and robust long-term SLAM by addressing pose estimation drift and degeneration.
This article begins by formulating loop closing and relocalization within a
unified framework. Then, we propose a novel multi-head network LCR-Net to
tackle both tasks effectively. It exploits novel feature extraction and
pose-aware attention mechanism to precisely estimate similarities and 6-DoF
poses between pairs of LiDAR scans. In the end, we integrate our LCR-Net into a
SLAM system and achieve robust and accurate online LiDAR SLAM in outdoor
driving environments. We thoroughly evaluate our LCR-Net through three setups
derived from loop closing and relocalization, including candidate retrieval,
closed-loop point cloud registration, and continuous relocalization using
multiple datasets. The results demonstrate that LCR-Net excels in all three
tasks, surpassing the state-of-the-art methods and exhibiting a remarkable
generalization ability. Notably, our LCR-Net outperforms baseline methods
without using a time-consuming robust pose estimator, rendering it suitable for
online SLAM applications. To our best knowledge, the integration of LCR-Net
yields the first LiDAR SLAM with the capability of deep loop closing and
relocalization. The implementation of our methods will be made open-source.

该研究聚焦于解决姿态估计漂移和退化问题，提出了一种名为 LCR-Net 的多头网络模型，通过新颖的特征提取和姿态感知机制来准确估计 LiDAR 扫描之间的相似度和 6-DoF 姿态，将其应用于室外驾驶环境中，实现了强大而准确的在线 LiDAR SLAM，同时在候选项检索、闭环点云配准和连续重定位等三个设置中全面评估并展示了优异的性能，超过了同类方法并表现出卓越的泛化能力，它不依赖于耗时的鲁棒姿态估计器，适用于在线 SLAM 应用。这是首个具有深度闭环检测和重定位能力的 LiDAR SLAM。

快速准确的深度闭环和可靠的激光雷达定位与建图

Fast and Accurate Deep Loop Closing and Relocalization for Reliable  LiDAR SLAM

We propose an end-to-end architecture for joint 2D and 3D human pose
estimation in natural images. Key to our approach is the generation and scoring
of a number of pose proposals per image, which allows us to predict 2D and 3D
poses of multiple people simultaneously. Hence, our approach does not require
an approximate localization of the humans for initialization. Our
Localization-Classification-Regression architecture, named LCR-Net, contains 3
main components: 1) the pose proposal generator that suggests candidate poses
at different locations in the image; 2) a classifier that scores the different
pose proposals; and 3) a regressor that refines pose proposals both in 2D and
3D. All three stages share the convolutional feature layers and are trained
jointly. The final pose estimation is obtained by integrating over neighboring
pose hypotheses, which is shown to improve over a standard non maximum
suppression algorithm. Our method recovers full-body 2D and 3D poses,
hallucinating plausible body parts when the persons are partially occluded or
truncated by the image boundary. Our approach significantly outperforms the
state of the art in 3D pose estimation on Human3.6M, a controlled environment.
Moreover, it shows promising results on real images for both single and
multi-person subsets of the MPII 2D pose benchmark and demonstrates satisfying
3D pose results even for multi-person images.

本文提出了一种 LCR-Net 架构，包括姿态候选生成器、姿态候选分类器和姿态回归器，可以同时预测多个人的 2D 和 3D 姿态，并且不需要初始人类本地化。该方法在姿态估计的表现上显著优于现有技术，特别是具有局部遮挡或图像边界截断的人体部分的情况。