.png){kind=icon}
Return
Fusing Horizon Information for Visual Localization
.png){kind=icon}
OA
AI
DOI：10.3390/ai6060121.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Localization is the foundation and core of autonomous driving. Current visual localization methods rely heavily on high-definition maps. However, high-definition maps are not only costly but also have poor real-time performance. In autonomous driving, place recognition is equally crucial and of great significance. Existing place recognition methods are deficient in local feature extraction and position and orientation errors can occur during the matching process. To address these limitations, this paper presents a robust multi-dimensional feature fusion framework for place recognition. Unlike existing methods such as OrienterNet, which homogenously process images and maps at the underlying feature level while neglecting modal disparities, our framework—applied to existing 2D maps—introduces a heterogeneous structural-semantic approach inspired by OrienterNet. It employs structured Stixel features (containing positional information) to capture image geometry, while representing the OSM environment through polar coordinate-based building distributions. Dedicated encoders are designed to adapt to each modality. Additionally, global relational features are generated by computing distances and angles between the current position and building pixels in the map, providing the system with detailed spatial relationship information. Subsequently, individual Stixel features are rotationally matched with global relations to achieve feature matching at diverse angles. During the BEV map matching process in OrienterNet, visual localization relies primarily on horizontal image information. In contrast, the novel method proposed herein performs matching based on vertical image information while fusing horizontal cues to complete place recognition. Extensive experimental results demonstrate that the proposed method significantly outperforms the mentioned state-of-the-art approaches in localization accuracy, effectively resolving the existing limitations.
Keywords:
visual localization
place recognition
multi-dimensional feature fusion
Stixel features
OSM environment
Journal
A
IF:
5
Papers:
1.1K
Citations:
941
Organization
Cited Papers
.png){kind=icon}
ShareSaveShareSave