In recent decades, visual simultaneous localization and mapping (vSLAM) has gained
significant interest in both academia and industry. It estimates camera motion and …

Unsupervised monocular depth and ego-motion estimation has drawn extensive research
attention in recent years. Although current methods have reached a high up-to-scale …

Deep-learning-based localization and mapping approaches have recently emerged as a
new research direction and receive significant attention from both industry and academia …

Depth estimation, visual odometry (VO), and bird's-eye-view (BEV) scene layout estimation
present three critical tasks for driving scene perception, which is fundamental for motion …

This paper proposes an illumination-robust visual odometry (VO) system that incorporates
both accelerated learning-based corner point algorithms and an extended line feature …

Simultaneous Localization and Mapping (SLAM) is a crucial function for most autonomous
systems, allowing them to both navigate through and create maps of unfamiliar …

In this paper, we introduce a novel approach for ground plane normal estimation of wheeled
vehicles. In practice, the ground plane is dynamically changed due to braking and unstable …

Remarkable progress has been made in self-supervised monocular depth estimation (SS-
MDE) by exploring cross-view consistency, eg, photometric consistency and 3D point cloud …

Purpose This paper aims to provide a better understanding of the challenges and potential
solutions in Visual Simultaneous Localization and Mapping (SLAM), laying the foundation …

Visual Odometry (VO) is a crucial process for estimating camera motion in real-time based
on visual information captured. The emergence of deep learning has significantly …