Popular GitHub Repositories for Inertial Sensors

Human Activity Recognition example using TensorFlow on smartphone sensors dataset and an LSTM RNN. Classifying the type of movement amongst six activity categories - Guillaume Chevalier

Roadmap to become a Visual-SLAM developer in 2023

LeGO-LOAM, LIO-SAM, LVI-SAM, FAST-LIO2, Faster-LIO, VoxelMap, R3LIVE, Point-LIO, KISS-ICP, DLO, DLIO, Ada-LIO, PV-LIO, SLAMesh, ImMesh, FAST-LIO-MULTI, M-LOAM, LOCUS, SLICT, MA-LIO, CT-ICP, GenZ-ICP, iG-LIO, SR-LIO application and comparison on Gazebo and real-world datasets. Installation and config files are provided.

The project can achieve FCWS, LDWS, and LKAS functions solely using only visual sensors. using YOLOv5 / YOLOv5-lite / YOLOv6 / YOLOv7 / YOLOv8 / YOLOv9 / EfficientDet and Ultra-Fast-Lane-Detection-v2 .

Tensorflow and PyTorch implementation of Unsupervised Depth Completion from Visual Inertial Odometry (in RA-L January 2020 & ICRA 2020)

Code for "Photometric Visual-Inertial Navigation with Uncertainty-Aware Ensembles" in TRO 2022

Deep Learning-Based Gait Recognition Using Smartphones in the Wild

Package towards building Explainable Forecasting and Nowcasting Models with State-of-the-art Deep Neural Networks and Dynamic Factor Model on Time Series data sets with single line of code. Also, provides utilify facility for time-series signal similarities matching, and removing noise from timeseries signals.

首个基于LiDAR里程计的六自由度样条递归运动估计器

Dockerized ROS2 stack for the WATonomous Autonomous Driving Software Pipeline

A fully-annotated, open-design dataset of autonomous and piloted high-speed flight

The official implementation of our ECCV 2020 paper "Learning Camera-Aware Noise Models".

Introducing various sensors to our Gazebo Harmonic simulation

Visual-Inertial-Semantic-MApping Dataset and tools

TinyOdom: Hardware-Aware Efficient Neural Inertial Navigation

Reference github repository for the paper "Learning to Reduce Defocus Blur by Realistically Modeling Dual-Pixel Data". We propose a procedure to generate realistic DP data synthetically. Our synthesis approach mimics the optical image formation found on DP sensors and can be applied to virtual scenes rendered with standard computer software. Leveraging these realistic synthetic DP images, we introduce a new recurrent convolutional network (RCN) architecture that can improve defocus deblurring results and is suitable for use with single-frame and multi-frame data captured by DP sensors.

Implementation of the paper Using Synthetic Data to Enhance the Accuracy of Fingerprint-Based Localization: A Deep Learning Approach (IEEE Sensors Letters, 2020), which utilizes Generative Adversarial Networks (GANs) for indoor localiztaion.

A1 SLAM: Quadruped SLAM using the A1's onboard sensors

False Data Injection Attacks in Internet of Things and Deep Learning enabled Predictive Analytics

Intelligent Vehicle Perception Based on Inertial Sensing and Artificial Intelligence