Accurate and robust localization remains a significant challenge for
autonomous vehicles. The cost of sensors and limitations in local computational
efficiency make it difficult to scale to large commercial applications.
Traditional vision-based approaches focus on texture features that are
susceptible to changes in lighting, season, perspective, and appearance.
Additionally, the large storage size of maps with descriptors and complex
optimization processes hinder system performance. To balance efficiency and
accuracy, we propose a novel lightweight visual semantic localization algorithm
that employs stable semantic features instead of low-level texture features.
First, semantic maps are constructed offline by detecting semantic objects,
such as ground markers, lane lines, and poles, using cameras or LiDAR sensors.
Then, online visual localization is performed through data association of
semantic features and map objects. We evaluated our proposed localization
framework in the publicly available KAIST Urban dataset and in scenarios
recorded by ourselves. The experimental results demonstrate that our method is
a reliable and practical localization solution in various autonomous driving
localization tasks.

提出了一种新颖的轻量级视觉语义定位算法，通过稳定的语义特征代替低级纹理特征，结合离线构建的语义地图和在线的数据关联实现实时的自动驾驶定位。实验结果表明，该方法在各种自动驾驶定位任务中是可靠且实用的定位解决方案。

自主车辆的单目语义地图定位

Monocular Localization with Semantics Map for Autonomous Vehicles

Recognizing multiple labels of images is a fundamental but challenging task
in computer vision, and remarkable progress has been attained by localizing
semantic-aware image regions and predicting their labels with deep
convolutional neural networks. The step of hypothesis regions (region
proposals) localization in these existing multi-label image recognition
pipelines, however, usually takes redundant computation cost, e.g., generating
hundreds of meaningless proposals with non-discriminative information and
extracting their features, and the spatial contextual dependency modeling among
the localized regions are often ignored or over-simplified. To resolve these
issues, this paper proposes a recurrent attention reinforcement learning
framework to iteratively discover a sequence of attentional and informative
regions that are related to different semantic objects and further predict
label scores conditioned on these regions. Besides, our method explicitly
models long-term dependencies among these attentional regions that help to
capture semantic label co-occurrence and thus facilitate multi-label
recognition. Extensive experiments and comparisons on two large-scale
benchmarks (i.e., PASCAL VOC and MS-COCO) show that our model achieves superior
performance over existing state-of-the-art methods in both performance and
efficiency as well as explicitly identifying image-level semantic labels to
specific object regions.

本研究提出了一种循环注意强化学习框架，通过迭代地发现一系列关注和信息区域，以及进一步预测这些区域中的标签得分，从而实现多标签图像识别。