There are 45 repositories under sensor-fusion topic.
[ICRA'23] BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation
[PAMI'23] TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Driving; [CVPR'21] Multi-Modal Fusion Transformer for End-to-End Autonomous Driving
alfred-py: A deep learning utility library for **human**, more detail about the usage of lib to: https://zhuanlan.zhihu.com/p/341446046
Implementation of Tightly Coupled 3D Lidar Inertial Odometry and Mapping (LIO-mapping)
X Inertial-aided Visual Odometry
Tightly coupled GNSS-Visual-Inertial system for locally smooth and globally consistent state estimation in complex environment.
IMU + X(GNSS, 6DoF Odom) Loosely-Coupled Fusion Localization based on ESKF, IEKF, UKF(UKF/SPKF, JUKF, SVD-UKF) and MAP
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.
An in-depth step-by-step tutorial for implementing sensor fusion with robot_localization! đź›°
Predict dense depth maps from sparse and noisy LiDAR frames guided by RGB images. (Ranked 1st place on KITTI) [2019]
HybVIO visual-inertial odometry and SLAM system
Official code for "EagerMOT: 3D Multi-Object Tracking via Sensor Fusion" [ICRA 2021]
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.
Kalman filter, sensor fusion
ROS package for the Perception (Sensor Processing, Detection, Tracking and Evaluation) of the KITTI Vision Benchmark Suite
A graph-based multi-sensor fusion framework. It can be used to fuse various relative or absolute measurments with IMU readings in real-time.
TI mmWave radar ROS driver (with sensor fusion and hybrid)
Modular, open-source implementations of continuous-time simultaneous localization and mapping algorithms.
Vehicle State Estimation using Error-State Extended Kalman Filter
Deep learning approach for estimation of Remaining Useful Life (RUL) of an engine
Ground-Fusion: A Low-cost Ground SLAM System Robust to Corner Cases (ICRA2024)
Unscented Kalman Filtering on (Parallelizable) Manifolds (UKF-M)
A simple implementation of some complex Sensor Fusion algorithms
Tensorflow and PyTorch implementation of Unsupervised Depth Completion from Visual Inertial Odometry (in RA-L January 2020 & ICRA 2020)
Loosely coupled integration of GNSS and IMU
[IROS 2023] Fast LiDAR-Inertial Odometry via Incremental Plane Pre-Fitting and Skeleton Tracking
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).
Filters: KF, EKF, UKF || Process Models: CV, CTRV || Measurement Models: Radar, Lidar
GLIO: Tightly-Coupled GNSS/LiDAR/IMU Integration for Continuous and Drift-free State Estimation
A Sensor Fusion Algorithm that can predict a State Estimate and Update if it is uncertain