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Found 7 papers, 3 papers with code
Hierarchical Segment-based Optimization for SLAM
This paper presents a hierarchical segment-based optimization method for Simultaneous Localization and Mapping (SLAM) system.
Continual Neural Mapping: Learning An Implicit Scene Representation from Sequential Observations
We introduce an experience replay approach to tackle an exemplary task of continual neural mapping: approximating a continuous signed distance function (SDF) from sequential depth images as a scene geometry representation.
A Collaborative Visual SLAM Framework for Service Robots
We present a collaborative visual simultaneous localization and mapping (SLAM) framework for service robots.
RaP-Net: A Region-wise and Point-wise Weighting Network to Extract Robust Features for Indoor Localization
Experimental results show that the proposed RaP-Net trained with OpenLORIS-Location dataset achieves excellent performance in the feature matching task and significantly outperforms state-of-the-arts feature algorithms in indoor localization.
DXSLAM: A Robust and Efficient Visual SLAM System with Deep Features
For visual SLAM algorithms, though the theoretical framework has been well established for most aspects, feature extraction and association is still empirically designed in most cases, and can be vulnerable in complex environments.
Loop Closure Detection
Simultaneous Localization and Mapping
OpenLORIS-Object: A Robotic Vision Dataset and Benchmark for Lifelong Deep Learning
Yet, robotic vision poses unique challenges for applying visual algorithms developed from these standard computer vision datasets due to their implicit assumption over non-varying distributions for a fixed set of tasks.
Are We Ready for Service Robots? The OpenLORIS-Scene Datasets for Lifelong SLAM
We also design benchmarking metrics for lifelong SLAM, with which the robustness and accuracy of pose estimation are evaluated separately.
