Deep visual teach and repeat: Employing deep models for navigation based on teaching path
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Updated
Nov 9, 2018 - Python
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
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
LPD-Net: 3D Point Cloud Learning for Large-Scale Place Recognition and Environment Analysis (ICCV 2019)
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
(RSS 2018) LoST - Visual Place Recognition using Visual Semantics for Opposite Viewpoints across Day and Night
🗺️ Place Recognition method based on radar scan images
[ECCV-2020 (spotlight)] Self-supervising Fine-grained Region Similarities for Large-scale Image Localization. 🌏 PyTorch open-source toolbox for image-based localization (place recognition).
Implementation of IROS20 paper - "Semantic Graph Based Place Recognition for 3D Point Clouds"
MinkLoc3D-SI: 3D LiDAR place recognition with sparse convolutions,spherical coordinates, and intensity
RadarLoc: Large-Scale Topological Radar Localization Using Learned Descriptors
EgoNN: Egocentric Neural Network for Point Cloud Based 6DoF Relocalization at the City Scale
Differentiable Scan Context with Orientation
Benchmark datasets used in ICRA 2020 paper: Fast, Compact and Highly Scalable Visual Place Recognition through Sequence-based Matching of Overloaded Representations
The official code and datasets for "Zero-Shot Multi-View Indoor Localization via Graph Location Networks" (ACMMM 2020)
Code for the CoRL 2021 paper "SeqMatchNet: Contrastive Learning with Sequence Matching for Place Recognition \& Relocalization"
PointNetVLAD-FiLM: Implicit ensemble implementation using FiLM-Ensemble
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