Robust and accurate pose estimation in unknown environments is an essential
part of robotic applications. We focus on LiDAR-based point-to-point ICP
combined with effective semantic information. This paper proposes a novel
semantic information-assisted ICP method named SAGE-ICP, which leverages
semantics in odometry. The semantic information for the whole scan is timely
and efficiently extracted by a 3D convolution network, and these point-wise
labels are deeply involved in every part of the registration, including
semantic voxel downsampling, data association, adaptive local map, and dynamic
vehicle removal. Unlike previous semantic-aided approaches, the proposed method
can improve localization accuracy in large-scale scenes even if the semantic
information has certain errors. Experimental evaluations on KITTI and KITTI-360
show that our method outperforms the baseline methods, and improves accuracy
while maintaining real-time performance, i.e., runs faster than the sensor
frame rate.

该论文介绍了一种名为 SAGE-ICP 的新型语义信息辅助 ICP 方法，通过三维卷积网络提取全扫描的及时有效的语义信息，并将这些点级标签深入应用于配准的各个部分，包括语义体素下采样、数据关联、自适应局部地图和动态车辆去除，实验证明该方法在大规模场景中可以提高定位的准确性，即使语义信息存在一定误差，同时保持实时性能，即快于传感器帧速率。

SAGE-ICP: 语义信息辅助 ICP

SAGE-ICP: Semantic Information-Assisted ICP

We introduce a technique for pairwise registration of neural fields that
extends classical optimization-based local registration (i.e. ICP) to operate
on Neural Radiance Fields (NeRF) -- neural 3D scene representations trained
from collections of calibrated images. NeRF does not decompose illumination and
color, so to make registration invariant to illumination, we introduce the
concept of a ''surface field'' -- a field distilled from a pre-trained NeRF
model that measures the likelihood of a point being on the surface of an
object. We then cast nerf2nerf registration as a robust optimization that
iteratively seeks a rigid transformation that aligns the surface fields of the
two scenes. We evaluate the effectiveness of our technique by introducing a
dataset of pre-trained NeRF scenes -- our synthetic scenes enable quantitative
evaluations and comparisons to classical registration techniques, while our
real scenes demonstrate the validity of our technique in real-world scenarios.
Additional results available at: this https URL

介绍了一种基于神经辐射场的配对注册技术，通过引入表面场的概念与鲁棒优化的算法来实现 NeRF 场景的配准，该技术可以应用于不同领域的任务。

神经辐射场的配对对齐

nerf2nerf: Pairwise Registration of Neural Radiance Fields

We introduce a novel self-attention-based normal estimation network that is
able to focus softly on relevant points and adjust the softness by learning a
temperature parameter, making it able to work naturally and effectively within
a large neighbourhood range. As a result, our model outperforms all existing
normal estimation algorithms by a large margin, achieving 94.1% accuracy in
comparison with the previous state of the art of 91.2%, with a 25x smaller
model and 12x faster inference time. We also use point-to-plane Iterative
Closest Point (ICP) as an application case to show that our normal estimations
lead to faster convergence than normal estimations from other methods, without
manually fine-tuning neighbourhood range parameters. Code available at
this https URL

我们介绍了一种基于自我注意力机制的新型网络，可以软性聚焦于相关点，并通过学习温度参数来调整软性，使其能够在较大的近邻范围内自然而有效地工作。结果，我们的模型比所有现有的正常估计算法表现更好，精度达到 94.1%，比以前的 91.2% 的最高水平快 25 倍小，推理时间快 12 倍。我们还使用点对平面迭代最近点（ICP）作为应用案例，以展示我们的正常估计比其他方法的正常估计更快地收敛，而无需手动微调近邻参数范围。