sensor-fusion

Arduino sketches for MPU9250 9DoF with AHRS sensor fusion

A Robust, Real-time, RGB-colored, LiDAR-Inertial-Visual tightly-coupled state Estimation and mapping package

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

X Inertial-aided Visual Odometry

alfred-py: A deep learning utility library for **human**, more detail about the usage of lib to: https://zhuanlan.zhihu.com/p/341446046

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

[CVPR'21] Multi-Modal Fusion Transformer for End-to-End Autonomous Driving

Tightly coupled GNSS-Visual-Inertial system for locally smooth and globally consistent state estimation in complex environment.

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.

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

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

IMU + X(GNSS, 6DoF Odom) Loosely-Coupled Fusion Localization based on EKF(ESKF, IEKF), UKF(UKF/SPKF, JUKF, SVD-UKF) and MAP

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.

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

Kalman filter, sensor fusion (Self-Driving Car Nanodegree, Sensor Fusion Nanodegree)

Official code for "EagerMOT: 3D Multi-Object Tracking via Sensor Fusion" [ICRA 2021]

Open-source Autonomy Software in Rust-lang with gRPC for the Roomba series robot vacuum cleaners. Under development.

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

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

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

HybVIO visual-inertial odometry and SLAM system

Loosely coupled integration of GNSS and IMU

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

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

Vehicle State Estimation using Error-State Extended Kalman Filter

Cooperative Driving Dataset: a dataset for multi-agent driving scenarios

A simple implementation of some complex Sensor Fusion algorithms

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

State estimation and filtering algorithms in Go

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