Arduino sketches for MPU9250 9DoF with AHRS sensor fusion

X Inertial-aided Visual Odometry

Implementation of Tightly Coupled 3D Lidar Inertial Odometry and Mapping (LIO-mapping)

alfred-py: A deep learning utility library for visualization and sensor fusion purpose

Robocentric Visual-Inertial Odometry (IJRR2019, IROS2018)

An in-depth step-by-step tutorial for implementing sensor fusion with robot_localization! 🛰

Predict dense depth maps from sparse and noisy LiDAR frames guided by RGB images. Ranked 1st place on KITTI. (MVA 2019 Conference)

ROS package for the Perception (Sensor Processing, Detection, Tracking and Evaluation) of the KITTI Vision Benchmark Suite

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.

This is a package for extrinsic calibration between a 3D LiDAR and a camera, described in paper: Improvements to Target-Based 3D LiDAR to Camera Calibration. This package is used for Cassie Blue's 3D LiDAR semantic mapping and automation.

Deep learning approach for estimation of Remaining Useful Life (RUL) of an engine

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

Loosely coupled integration of GNSS and IMU

TI mmWave radar ROS driver (with sensor fusion and hybrid)

An extended Kalman Filter implementation in C++ for fusing lidar and radar sensor measurements.

A Sensor Fusion Algorithm that can predict a State Estimate and Update if it is uncertain

Unscented Kalman Filtering on (Parallelizable) Manifolds (UKF-M)

LiLi-OM is a tightly-coupled, keyframe-based LiDAR-inertial odometry and mapping system for both solid-state-LiDAR and conventional LiDARs.

State estimation and filtering algorithms in Go

algorithms for synchronizing clocks

Filters: KF, EKF, UKF || Process Models: CV, CTRV || Measurement Models: Radar, Lidar

Code for 'RRPN: Radar Region Proposal Network for Object Detection in Autonomous Vehicles' (ICIP 2019)

Udacity's Self-Driving Car Nanodegree project files and notes.

AHRS (Attitude Heading Reference Systems) calculation for JavaScript

Vehicle State Estimation using Error-State Extended Kalman Filter

A simple implementation of some complex Sensor Fusion algorithms

Code for "CMRNet: Camera to LiDAR-Map Registration" (ITSC 2019) - WIP

Strongly-typed, dependency based application framework for code/data separation with dependency injection and data passing.

State Estimation and Localization of an autonomous vehicle based on IMU (high rate), GNSS (GPS) and Lidar data with sensor fusion techniques using the Extended Kalman Filter (EKF).

Sensor Fusion and Localization related projects of Udacity's Self-driving Car Nanodegree Program: