The availability of real-time semantics greatly improves the core geometric
functionality of SLAM systems, enabling numerous robotic and AR/VR
applications. We present a new methodology for real-time semantic mapping from
RGB-D sequences that combines a 2D neural network and a 3D network based on a
SLAM system with 3D occupancy mapping. When segmenting a new frame we perform
latent feature re-projection from previous frames based on differentiable
rendering. Fusing re-projected feature maps from previous frames with
current-frame features greatly improves image segmentation quality, compared to
a baseline that processes images independently. For 3D map processing, we
propose a novel geometric quasi-planar over-segmentation method that groups 3D
map elements likely to belong to the same semantic classes, relying on surface
normals. We also describe a novel neural network design for lightweight
semantic map post-processing. Our system achieves state-of-the-art semantic
mapping quality within 2D-3D networks-based systems and matches the performance
of 3D convolutional networks on three real indoor datasets, while working in
real-time. Moreover, it shows better cross-sensor generalization abilities
compared to 3D CNNs, enabling training and inference with different depth
sensors. Code and data will be released on project page:
this http URL

提出了一种基于 2D 和 3D SLAM 网络结合的实时语义地图方法，通过重新投影及渲染后进行特征融合，以及一种利用表面法线的几何分割方法来整合 3D 语义元素，并使用新型的神经网络进行轻量级的语义地图后处理，实现了在不同深度感应器方面的性能表现和交叉传感器通用性方面的优化。

使用潜在先验网络和准平面分割的实时语义映射

SeMLaPS: Real-time Semantic Mapping with Latent Prior Networks and  Quasi-Planar Segmentation

Camera relocalization is the key component of simultaneous localization and
mapping (SLAM) systems. This paper proposes a learning-based approach, named
Sparse Spatial Scene Embedding with Graph Neural Networks (S3E-GNN), as an
end-to-end framework for efficient and robust camera relocalization. S3E-GNN
consists of two modules. In the encoding module, a trained S3E network encodes
RGB images into embedding codes to implicitly represent spatial and semantic
embedding code. With embedding codes and the associated poses obtained from a
SLAM system, each image is represented as a graph node in a pose graph. In the
GNN query module, the pose graph is transformed to form a embedding-aggregated
reference graph for camera relocalization. We collect various scene datasets in
the challenging environments to perform experiments. Our results demonstrate
that S3E-GNN method outperforms the traditional Bag-of-words (BoW) for camera
relocalization due to learning-based embedding and GNN powered scene matching
mechanism.

本文提出了一种名为 “稀疏空间场景嵌入以图神经网络为主体的学习方法（S3E-GNN）” 的端到端框架，用于相机重定位。该方法使用编码模块和查询模块，将场景图嵌入到参考图中进行相机重定位。实验结果表明，由于基于学习的嵌入和 GNN 增强的场景匹配机制，S3E-GNN 方法优于传统的词袋（BoW）方法。

S3E-GNN：利用图神经网络的稀疏空间情景嵌入进行相机重定位

S3E-GNN: Sparse Spatial Scene Embedding with Graph Neural Networks for Camera Relocalization

Reliable and efficient Visual Place Recognition is a major building block of
modern SLAM systems. Leveraging on our prior work, in this paper we present a
Hamming Distance embedding Binary Search Tree (HBST) approach for binary
Descriptor Matching and Image Retrieval. HBST allows for descriptor Search and
Insertion in logarithmic time by exploiting particular properties of binary
Feature descriptors. We support the idea behind our search structure with a
thorough analysis on the exploited descriptor properties and their effects on
completeness and complexity of search and insertion. To validate our claims we
conducted comparative experiments for HBST and several state-of-the-art methods
on a broad range of publicly available datasets. HBST is available as a compact
open-source C++ header-only library.

本文介绍了一种基于 HBST 方法的二进制描述符匹配和图像检索技术，在日志时间内支持描述符搜索和插入，HBST 是一种紧凑的开源 C++ 头文件库，能够有效地应用于现代 SLAM 系统中的可靠和高效的视觉地点识别。