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ReLoc-PDR: Visual Relocalization Enhanced Pedestrian Dead Reckoning via Graph Optimization
Accurately and reliably positioning pedestrians in satellite-denied conditions remains a significant challenge.
Deep Learning for Inertial Positioning: A Survey
Inertial sensors are widely utilized in smartphones, drones, robots, and IoT devices, playing a crucial role in enabling ubiquitous and reliable localization.
SelfOdom: Self-supervised Egomotion and Depth Learning via Bi-directional Coarse-to-Fine Scale Recovery
To address this, we propose SelfOdom, a self-supervised dual-network framework that can robustly and consistently learn and generate pose and depth estimates in global scale from monocular images.
EMA-VIO: Deep Visual-Inertial Odometry with External Memory Attention
Accurate and robust localization is a fundamental need for mobile agents.
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