In 3D LiDAR-based robot self-localization, pole-like landmarks are gaining
popularity as lightweight and discriminative landmarks. This work introduces a
novel approach called "discriminative rotation-invariant poles," which enhances
the discriminability of pole-like landmarks while maintaining their lightweight
nature. Unlike conventional methods that model a pole landmark as a 3D line
segment perpendicular to the ground, we propose a simple yet powerful approach
that includes not only the line segment's main body but also its surrounding
local region of interest (ROI) as part of the pole landmark. Specifically, we
describe the appearance, geometry, and semantic features within this ROI to
improve the discriminability of the pole landmark. Since such pole landmarks
are no longer rotation-invariant, we introduce a novel rotation-invariant
convolutional neural network that automatically and efficiently extracts
rotation-invariant features from input point clouds for recognition.
Furthermore, we train a pole dictionary through unsupervised learning and use
it to compress poles into compact pole words, thereby significantly reducing
real-time costs while maintaining optimal self-localization performance. Monte
Carlo localization experiments using publicly available NCLT dataset
demonstrate that the proposed method improves a state-of-the-art pole-based
localization framework.

本文提出了一种名为 “具有鉴别性的旋转不变极点” 的新方法，它增强了轻量级且具有鉴别性的极点的特征，在保持其轻量级的同时提高了其鉴别性。通过在极点中包括其主体线段以及周围感兴趣区域（ROI）来改进极点的鉴别性，同时描述了该 ROI 内的外观、几何和语义特征以提高极点的鉴别性。此外，引入了一种新型的旋转不变卷积神经网络来从输入点云中自动有效地提取旋转不变特征，还通过无监督学习训练一个极点字典，并将极点压缩成紧凑的极点词，从而在保持最佳自我定位性能的同时显著降低了实时成本。使用公开可用的 NCLT 数据集进行的蒙特卡洛定位实验表明，所提出的方法改进了现有极点定位框架的性能。

DRIP: 3D LiDAR 定位的区分旋转不变的杆状地标描述符

DRIP: Discriminative Rotation-Invariant Pole Landmark Descriptor for 3D  LiDAR Localization

We present Loc-NeRF, a real-time vision-based robot localization approach
that combines Monte Carlo localization and Neural Radiance Fields (NeRF). Our
system uses a pre-trained NeRF model as the map of an environment and can
localize itself in real-time using an RGB camera as the only exteroceptive
sensor onboard the robot. While neural radiance fields have seen significant
applications for visual rendering in computer vision and graphics, they have
found limited use in robotics. Existing approaches for NeRF-based localization
require both a good initial pose guess and significant computation, making them
impractical for real-time robotics applications. By using Monte Carlo
localization as a workhorse to estimate poses using a NeRF map model, Loc-NeRF
is able to perform localization faster than the state of the art and without
relying on an initial pose estimate. In addition to testing on synthetic data,
we also run our system using real data collected by a Clearpath Jackal UGV and
demonstrate for the first time the ability to perform real-time global
localization with neural radiance fields. We make our code publicly available
at this https URL

本篇论文介绍了一个结合蒙特卡罗定位 (Monte Carlo localization) 和神经辐射场模型 (Neural Radiance Fields) 的机器人实时视觉定位方法 ——Loc-NeRF，它不依赖于初始位姿估计，可以使用机器人上的 RGB 相机在预训练的 NeRF 模型地图上实现实时本地化，相比现有技术能够更快地完成本地化。我们通过 Clearpath Jackal UGV 的真实数据验证了该方法的有效性，并将代码公开在一个 https URL 上。