A Review of Visual Odometry Methods and Its Applications for Autonomous Driving
원문 초록
The research into autonomous driving applications has observed an increase in computer vision-based approaches in recent years. In attempts to develop exclusive vision-based systems, visual odometry is often considered as a key element to achieve motion estimation and self-localisation, in place of wheel odometry or inertial measurements. This paper presents a recent review to methods that are pertinent to visual odometry with an emphasis on autonomous driving. This review covers visual odometry in their monocular, stereoscopic and visual-inertial form, individually presenting them with analyses related to their applications. Discussions are drawn to outline the problems faced in the current state of research, and to summarise the works reviewed. This paper concludes with future work suggestions to aid prospective developments in visual odometry.
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DINO-VO: A Feature-based Visual Odometry Leveraging a Visual Foundation Model
ZeroVO: Visual Odometry with Minimal Assumptions
LifelongPR: Lifelong knowledge fusion for point cloud place recognition based on replay and prompt learning
$I^{2}$-World: Intra-Inter Tokenization for Efficient Dynamic 4D Scene Forecasting
HiM2SAM: Enhancing SAM2 with Hierarchical Motion Estimation and Memory Optimization towards Long-term Tracking
MCAM: Multimodal Causal Analysis Model for Ego-Vehicle-Level Driving Video Understanding
토론
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