{"type":"ScholarlyArticle","title":"BRIGHT-VO: Brightness-Guided Hybrid Transformer for Visual Odometry with Multi-modality Refinement Module","authors":[{"name":"Dongzhihan Wang","slug":"dongzhihan-wang","given":"Dongzhihan","family":"Wang","affiliation":""},{"name":"Yang Yang","slug":"yang-yang","given":"Yang","family":"Yang","affiliation":""},{"name":"Xuyang Chen","slug":"xuyang-chen","given":"Xuyang","family":"Chen","affiliation":""},{"name":"Liang Xu","slug":"liang-xu","given":"Liang","family":"Xu","affiliation":""}],"abstract":"Visual odometry (VO) plays a crucial role in autonomous driving, robotic navigation, and other related tasks by estimating the position and orientation of a camera based on visual input. Significant progress has been made in data-driven VO methods, particularly those leveraging deep learning techniques to extract image features and estimate camera poses. However, these methods often struggle in low-light conditions because of the reduced visibility of features and the increased difficulty of matching keypoints. To address this limitation, we introduce BrightVO, a novel VO model based on Transformer architecture, which not only performs front-end visual feature extraction, but also incorporates a multi-modality refinement module in the back-end that integrates Inertial Measurement Unit (IMU) data. Using pose graph optimization, this module iteratively refines pose estimates to reduce errors and improve both accuracy and robustness. Furthermore, we create a synthetic low-light dataset, KiC4R, which includes a variety of lighting conditions to facilitate the training and evaluation of VO frameworks in challenging environments. Experimental results demonstrate that BrightVO achieves state-of-the-art performance on both the KiC4R dataset and the KITTI benchmarks. Specifically, it provides an average improvement of 20% in pose estimation accuracy in normal outdoor environments and 25% in low-light conditions, outperforming existing methods. This work is open-source at https://github.com/Anastasiawd/BrightVO.","doi":"10.24963/ijcai.2025/216","url":"https://ijcai.org/proceedings/2025/216","pdf":"https://ijcai.org/proceedings/2025/0216.pdf","markdown":"https://ijcai.org/proceedings/2025/216.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/216","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Computer Vision","track":"Main Track","number":216,"pages":{"first":1936,"last":1944},"keywords":["Computer Vision: CV: Embodied vision: Active agents, simulation","Computer Vision: CV: Motion and tracking","Robotics: ROB: Robotics and vision","Robotics: ROB: Perception"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}