Binary feature descriptors have been widely used in various visual
measurement tasks, particularly those with limited computing resources and
storage capacities. Existing binary descriptors may not perform well for
long-term visual measurement tasks due to their sensitivity to illumination
variations. It can be observed that when image illumination changes
dramatically, the relative relationship among local patches mostly remains
intact. Based on the observation, consequently, this study presents an
illumination-insensitive binary (IIB) descriptor by leveraging the local
inter-patch invariance exhibited in multiple spatial granularities to deal with
unfavorable illumination variations. By taking advantage of integral images for
local patch feature computation, a highly efficient IIB descriptor is achieved.
It can encode scalable features in multiple spatial granularities, thus
facilitating a computationally efficient hierarchical matching from coarse to
fine. Moreover, the IIB descriptor can also apply to other types of image data,
such as depth maps and semantic segmentation results, when available in some
applications. Numerical experiments on both natural and synthetic datasets
reveal that the proposed IIB descriptor outperforms state-of-the-art binary
descriptors and some testing float descriptors. The proposed IIB descriptor has
also been successfully employed in a demo system for long-term visual
localization. The code of the IIB descriptor will be publicly available.

本研究提出了一种基于光照不变性的二进制特征描述符 (IIB)，通过利用多空间粒度中的局部跨补丁不变性来处理恶劣的光照变化，并采用积分图实现局部补丁特征计算的高效 IIB 描述符。数值实验结果表明，所提出的 IIB 描述符优于现有的二进制描述符和一些测试浮点描述符，并已成功应用于长期视觉定位的演示系统中。

基于局部块间不变性的光照不敏感二进制描述符用于视觉测量

Illumination-insensitive Binary Descriptor for Visual Measurement Based  on Local Inter-patch Invariance

In this paper we propose an approach to embed continuous and selector cues in
binary feature descriptors used for visual place recognition. The embedding is
achieved by extending each feature descriptor with a binary string that encodes
a cue and supports the Hamming distance metric. Augmenting the descriptors in
such a way has the advantage of being transparent to the procedure used to
compare them. We present two concrete applications of our methodology,
demonstrating the two considered types of cues. In addition to that, we
conducted on these applications a broad quantitative and comparative evaluation
covering five benchmark datasets and several state-of-the-art image retrieval
approaches in combination with various binary descriptor types.

本文提出了一种向二进制特征描述符中嵌入连续和选择器线索的方法，以支持视觉场地识别，通过使用两种不同类型的线索在五个基准数据集上和几种现有二进制描述符类型的结合下进行全面的定量和比较评估。

为视觉地点识别添加二进制特征描述符提示

Adding Cues to Binary Feature Descriptors for Visual Place Recognition

Many robotics applications require precise pose estimates despite operating
in large and changing environments. This can be addressed by visual
localization, using a pre-computed 3D model of the surroundings. The pose
estimation then amounts to finding correspondences between 2D keypoints in a
query image and 3D points in the model using local descriptors. However,
computational power is often limited on robotic platforms, making this task
challenging in large-scale environments. Binary feature descriptors
significantly speed up this 2D-3D matching, and have become popular in the
robotics community, but also strongly impair the robustness to perceptual
aliasing and changes in viewpoint, illumination and scene structure. In this
work, we propose to leverage recent advances in deep learning to perform an
efficient hierarchical localization. We first localize at the map level using
learned image-wide global descriptors, and subsequently estimate a precise pose
from 2D-3D matches computed in the candidate places only. This restricts the
local search and thus allows to efficiently exploit powerful non-binary
descriptors usually dismissed on resource-constrained devices. Our approach
results in state-of-the-art localization performance while running in real-time
on a popular mobile platform, enabling new prospects for robotics research.

该论文提出了一种利用深度学习技术进行分层定位的方法，仅在候选位置上计算 2D-3D 匹配的精确位姿估计，从而实现在流行的移动平台上实时运行并拥有最先进的本地化性能，为机器人研究带来了新的前景。