Place recognition with WiFi fingerprints using Autoencoders and Neural Networks
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Updated
Nov 6, 2017 - Jupyter Notebook
Place recognition with WiFi fingerprints using Autoencoders and Neural Networks
The implementation of ASMK retrieval approach on the Pittsburgh 250k place recognition data set
Deep visual teach and repeat: Employing deep models for navigation based on teaching path
Convolutional Autoencoder for Loop Closure
Visual Place Recognition implemented in PyTorch
Visual place recognition (VPR)/Loop closure detection (LCD) paper list.
PointNetVLAD: Deep Point Cloud Based Retrieval for Large-Scale Place Recognition, CVPR 2018
LPD-Net: 3D Point Cloud Learning for Large-Scale Place Recognition and Environment Analysis, ICCV 2019, Seoul, Korea
A Fast and Robust Place Recognition Approach for Stereo Visual Odometry using LiDAR Descriptors
Official implementation of paper "CityLearn: Diverse Real-World Environments for Sample-Efficient Navigation Policy Learning" by M. Chancán (ICRA 2020) https://doi.org/10.1109/ICRA40945.2020.9197336
A general framework for map-based visual localization. It contains 1) Map Generation which support traditional features or deeplearning features. 2) Hierarchical-Localizationvisual in visual(points or line) map. 3)Fusion framework with IMU, wheel odom and GPS sensors.
LPD-Net: 3D Point Cloud Learning for Large-Scale Place Recognition and Environment Analysis (ICCV 2019)
Ground Truth of KITTI dataset (odometry benchmark) for loop closure detection or visual place recognition
A curated list of Visual Place Recognition (VPR)/ loop closure detection (LCD) datasets
OpenSeqSLAM2 is a MATLAB toolbox for interactively exploring the visual place recognition problem.
Official PyTorch implementation of paper "A Hybrid Compact Neural Architecture for Visual Place Recognition" by M. Chancán (RA-L & ICRA 2020) https://doi.org/10.1109/LRA.2020.2967324
Light-weight place recognition and loop detection using road markings
(RSS 2018) LoST - Visual Place Recognition using Visual Semantics for Opposite Viewpoints across Day and Night
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