arXiv Computer Vision
Why does Deep Learning Improve Visual SLAM?
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
arXiv Computer Vision
Sep 22
Desc++: Efficient Descriptor Enhancement for Data Association in Existing Visual SLAM Systems
arXiv:2607.11099v2 Announce Type: replace-cross Abstract: Reliable visual data association is fundamental to visual SLAM (V-SLAM), as it directly determines the quality of the camera pose estimation...
arXiv Computer Vision
Sep 1
NGD-SLAM: Towards Real-Time Dynamic SLAM without GPU
arXiv:2405.07392v4 Announce Type: replace-cross Abstract: Many existing visual SLAM methods can achieve high localization accuracy in dynamic environments by leveraging deep learning to mask moving o...
arXiv AI
Jul 28
Semantic Semi-Incremental Data-Association-Free Object SLAM
arXiv:2607. 23384v1 Announce Type: cross Abstract: Data association between landmark measurements and landmark variables has long been a central challenge in SLAM, as estimation accuracy depends critically on associating measurements with the correct landmark variables.
arXiv Computer Vision
Sep 16
HuMemSLAM: Efficient Human-Inspired Semantic Place Recognition for Robust Visual SLAM
arXiv:2609.17168v1 Announce Type: cross Abstract: Autonomous systems require reliable place recognition for efficient and effective simultaneous localisation and mapping (SLAM). Traditional geometric...
arXiv Machine Learning
Jun 30
Generation of Uncertainty-Aware High-Level Spatial Concepts in Factorized 3D Scene Graphs via Graph Neural Networks
arXiv:2409. 11972v4 Announce Type: replace-cross Abstract: Enabling robots to autonomously discover high-level spatial concepts (e.
arXiv Computer Vision
Sep 23
End-to-End Visual Odometry with RNNs and Attention
The paper "End-to-End Visual Odometry with RNNs and Attention" presents a study of deep‑learning approaches to visual odometry (VO), proposing a novel temporal attention‑based model to enhance performance. It evaluates existing end‑to‑end VO methods and explores their effectiveness on hand‑held camera data, contrasting with the typical driving‑scene training sets. The work aims to improve VO accuracy in more dynamic and complex visual environments.