Recognizing places from an opposing viewpoint during a return trip is a
common experience for human drivers. However, the analogous robotics
capability, visual place recognition (VPR) with limited field of view cameras
under 180 degree rotations, has proven to be challenging to achieve. To address
this problem, this paper presents Same Place Opposing Trajectory (SPOT), a
technique for opposing viewpoint VPR that relies exclusively on structure
estimated through stereo visual odometry (VO). The method extends recent
advances in lidar descriptors and utilizes a novel double (similar and
opposing) distance matrix sequence matching method. We evaluate SPOT on a
publicly available dataset with 6.7-7.6 km routes driven in similar and
opposing directions under various lighting conditions. The proposed algorithm
demonstrates remarkable improvement over the state-of-the-art, achieving up to
91.7% recall at 100% precision in opposing viewpoint cases, while requiring
less storage than all baselines tested and running faster than all but one.
Moreover, the proposed method assumes no a priori knowledge of whether the
viewpoint is similar or opposing, and also demonstrates competitive performance
in similar viewpoint cases.

通過立體視覺測距法估計結構，並利用新的雙重距離矩陣序列匹配方法，提出了在有限視野下進行對立觀點的視覺地點識別方法，並在公開數據集上進行了評估，證明了其在對立觀點情況下相對其他方法具有顯著的改進，同時在類似觀點情況下也表現出競爭性能。

SPOT：基于点云的立体视觉地点识别（适用于类似和对立视点）

SPOT: Point Cloud Based Stereo Visual Place Recognition for Similar and  Opposing Viewpoints

Robust feature matching forms the backbone for most Visual Simultaneous
Localization and Mapping (vSLAM), visual odometry, 3D reconstruction, and
Structure from Motion (SfM) algorithms. However, recovering feature matches
from texture-poor scenes is a major challenge and still remains an open area of
research. In this paper, we present a Stereo Visual Odometry (StereoVO)
technique based on point and line features which uses a novel feature-matching
mechanism based on an Attention Graph Neural Network that is designed to
perform well even under adverse weather conditions such as fog, haze, rain, and
snow, and dynamic lighting conditions such as nighttime illumination and glare
scenarios. We perform experiments on multiple real and synthetic datasets to
validate the ability of our method to perform StereoVO under low visibility
weather and lighting conditions through robust point and line matches. The
results demonstrate that our method achieves more line feature matches than
state-of-the-art line matching algorithms, which when complemented with point
feature matches perform consistently well in adverse weather and dynamic
lighting conditions.

本文提出了一种基于点线特征的立体视觉里程计（StereoVO）技术，并使用一种基于注意力图神经网络的新型特征匹配机制，在恶劣的天气条件（如雾、霾、雨和雪）和动态光照条件（如夜间照明和眩光场景）下表现出色，通过稳健的点线匹配在低能见度天气和光照条件下完成立体视觉里程计。实验证明，与先进的线特征匹配算法相比，我们的方法能够获得更多的线特征匹配，而与点特征匹配相结合，在恶劣的天气和动态光照条件下表现一致优秀。

基于深度学习的点线特征匹配的立体视觉里程计与注意力图神经网络

Stereo Visual Odometry with Deep Learning-Based Point and Line Feature  Matching using an Attention Graph Neural Network

Accurate estimates of rotation are crucial to vision-based motion estimation
in augmented reality and robotics. In this work, we present a method to extract
probabilistic estimates of rotation from deep regression models. First, we
build on prior work and argue that a multi-headed network structure we name
HydraNet provides better calibrated uncertainty estimates than methods that
rely on stochastic forward passes. Second, we extend HydraNet to targets that
belong to the rotation group, SO(3), by regressing unit quaternions and using
the tools of rotation averaging and uncertainty injection onto the manifold to
produce three-dimensional covariances. Finally, we present results and analysis
on a synthetic dataset, learn consistent orientation estimates on the 7-Scenes
dataset, and show how we can use our learned covariances to fuse deep estimates
of relative orientation with classical stereo visual odometry to improve
localization on the KITTI dataset.

该论文提出了一种从深度回归模型中提取旋转概率估计的方法，并将其扩展到 SO (3) 旋转群的目标上，该方法在合成和真实数据中都获得了良好的结果。