Autonomous driving systems often require reliable loop closure detection to
guarantee reduced localization drift. Recently, 3D LiDAR-based localization
methods have used retrieval-based place recognition to find revisited places
efficiently. However, when deployed in challenging real-world scenarios, the
place recognition models become more complex, which comes at the cost of high
computational demand. This work tackles this problem from an
information-retrieval perspective, adopting a first-retrieve-then-re-ranking
paradigm, where an initial loop candidate ranking, generated from a 3D place
recognition model, is re-ordered by a proposed lightweight transformer-based
re-ranking approach (TReR). The proposed approach relies on global descriptors
only, being agnostic to the place recognition model. The experimental
evaluation, conducted on the KITTI Odometry dataset, where we compared TReR
with s.o.t.a. re-ranking approaches such as alphaQE and SGV, indicate the
robustness and efficiency when compared to alphaQE while offering a good
trade-off between robustness and efficiency when compared to SGV.

采用 Transformer-based re-ranking 方法，以全局描述为确定因素，实现了轻量化和有效的 Loop Closure Detection，与另外两种方法相比，在 KITTI Odometry 数据集上表现出更好的鲁棒性和效率。

TReR：用于 3D LiDAR 地方识别的轻量级 Transformer 重新排序方法

TReR: A Lightweight Transformer Re-Ranking Approach for 3D LiDAR Place  Recognition

LiDAR-based localization and mapping is one of the core components in many
modern robotic systems due to the direct integration of range and geometry,
allowing for precise motion estimation and generation of high quality maps in
real-time. Yet, as a consequence of insufficient environmental constraints
present in the scene, this dependence on geometry can result in localization
failure, happening in self-symmetric surroundings such as tunnels. This work
addresses precisely this issue by proposing a neural network-based estimation
approach for detecting (non-)localizability during robot operation. Special
attention is given to the localizability of scan-to-scan registration, as it is
a crucial component in many LiDAR odometry estimation pipelines. In contrast to
previous, mostly traditional detection approaches, the proposed method enables
early detection of failure by estimating the localizability on raw sensor
measurements without evaluating the underlying registration optimization.
Moreover, previous approaches remain limited in their ability to generalize
across environments and sensor types, as heuristic-tuning of degeneracy
detection thresholds is required. The proposed approach avoids this problem by
learning from a collection of different environments, allowing the network to
function over various scenarios. Furthermore, the network is trained
exclusively on simulated data, avoiding arduous data collection in challenging
and degenerate, often hard-to-access, environments. The presented method is
tested during field experiments conducted across challenging environments and
on two different sensor types without any modifications. The observed detection
performance is on par with state-of-the-art methods after environment-specific
threshold tuning.

提出了一种基于神经网络的方法，用于检测在机器人运行期间的定位问题，着重关注激光雷达测距定位模块，该方法通过模拟数据训练，可以适用于不同的环境和传感器类型，并在挑战性环境下进行了实地实验测试。

基于学习的本地可定位性估计用于强鲁棒性激光雷达定位

Learning-based Localizability Estimation for Robust LiDAR Localization

Compared to LiDAR-based localization methods, which provide high accuracy but
rely on expensive sensors, visual localization approaches only require a camera
and thus are more cost-effective while their accuracy and reliability typically
is inferior to LiDAR-based methods. In this work, we propose a vision-based
localization approach that learns from LiDAR-based localization methods by
using their output as training data, thus combining a cheap, passive sensor
with an accuracy that is on-par with LiDAR-based localization. The approach
consists of two deep networks trained on visual odometry and topological
localization, respectively, and a successive optimization to combine the
predictions of these two networks. We evaluate the approach on a new
challenging pedestrian-based dataset captured over the course of six months in
varying weather conditions with a high degree of noise. The experiments
demonstrate that the localization errors are up to 10 times smaller than with
traditional vision-based localization methods.

该论文提出了一种基于视觉和 LiDAR-based 的深度学习网络联合的、采用较为经济实惠的相机作为被动传感器的方法，将它们训练后进行优化以提高室外定位的精度，其误差比传统的视觉定位方法小至少 10 倍，通过对行人行为的不同研究，成功避免了多种不同时间、气候带来的问题。