Implementation of Contrastive Predictive Coding for Human Activity Recognition

Codes for "One-Dimensional Magnetic Field SLAM with Loop-Closure Detection"

[AAAI 2024] Official implementation of NavGPT: Explicit Reasoning in Vision-and-Language Navigation with Large Language Models

Lund University Vision, Radio, and Audio (LuViRA) Dataset

Deep Reinforcement Learning for mobile robot navigation in ROS Gazebo simulator. Using Twin Delayed Deep Deterministic Policy Gradient (TD3) neural network, a robot learns to navigate to a random g…

Millimeter Wave Wireless Assisted Robot Navigation with Link State Classification (arXiv preprint arXiv 2110.14789)

[CVPR 24'] Benchmarking Implicit Neural Representation and Geometric Rendering in Real-Time RGB-D SLAM

Incremental online learning-based localization and mapping using implicit representation formulated by neural radiance fields (NeRF)

为做NeRF-based SLAM毕设所读过的开源代码，尽量做到行行有注释。

This repo contains the code of the paper "Learned Inertial Odometry for Autonomous Drone Racing", RA-L 2023.

Tensorflow implementation for 'Joint Contrastive and Supervised Learning in Human Activity Recognition'

Dense Prediction Transformer for scale estimation in monocular visual odometry

UrbanLoco: A Full Sensor Suite Dataset for Mapping and Localization in Urban Scenes

Magnetic-Field aided Inertial Navigation System

MAINS: A Magnetic Field Aided Inertial Navigation System for Indoor Positioning

"🌐 Enhancing indoor navigation using Deep Learning. Predict accurate heading angles by fusing IMU sensor & GNSS data. Expanding architectures for precise direction estimation. Join me in advancing …

Custom Speedometer Dialog

Human Activity Recognition Transformer (HART) is a transformer based architecture that has been specifically adapted for IMU sensing devices. Findings shows that HART uses fewer paremeters, FLOPs a…

Algorithm for magnetometer correction

Three Symmetries for Data-Driven Pedestrian Inertial Navigation

Reproduce the paper “A Novel Deep Odometry Network for VehiclePositioning Based on Smartphone” method

Federated Learning with Noisy Labels

Human activity recognition using wearable sensors by heterogeneous convolution neural networks.