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

In this paper we propose a novel accurate method for dead-reckoning of
wheeled vehicles based only on an Inertial Measurement Unit (IMU). In the
context of intelligent vehicles, robust and accurate dead-reckoning based on
the IMU may prove useful to correlate feeds from imaging sensors, to safely
navigate through obstructions, or for safe emergency stops in the extreme case
of exteroceptive sensors failure. The key components of the method are the
Kalman filter and the use of deep neural networks to dynamically adapt the
noise parameters of the filter. The method is tested on the KITTI odometry
dataset, and our dead-reckoning inertial method based only on the IMU
accurately estimates 3D position, velocity, orientation of the vehicle and
self-calibrates the IMU biases. We achieve on average a 1.10% translational
error and the algorithm competes with top-ranked methods which, by contrast,
use LiDAR or stereo vision. We make our implementation open-source at:
this https URL

本文提出了一种新的基于惯性测量单元（IMU）的死推方法，关键组件为卡尔曼滤波器和深度神经网络，该方法在 KITTI 测距数据集上得到了测试，能够准确地估计车辆的 3D 位置、速度和方向，并自校准 IMU 偏差。