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Python implementation of LOAM (Lidar Odometry and Mapping) for rapid prototyping or educational purpose
Overview and list of works done in the field of state estimation using LiDAR , contains all popular LOAM implementations
Region Growing Segmentation/Color based segmentation/Point Cloud Correlation to detect objects in robot workspace.(Current configurations are for tracking violet coloured objects, the colour can be…
Point Cloud processing (VoxelGrid Downsampling, RANSAC Segmentation, KDTree Euclidean Clustering) for obstacle detection for autonomous vehicles.
The English version of 14 lectures on visual SLAM.
spinning Hokuyo form 3D point cloud
Fast LOAM: Fast and Optimized Lidar Odometry And Mapping for indoor/outdoor localization IROS 2021
A real-time LiDAR SLAM package that integrates FLOAM and ScanContext.
The official SuiteSparse library: a suite of sparse matrix algorithms authored or co-authored by Tim Davis, Texas A&M University.
A list of references on lidar point cloud processing for autonomous driving
Laser Odometry and Mapping (Loam) is a realtime method for state estimation and mapping using a 3D lidar.
A robust sensor fusion library for online localization.
GM-PHD filter implementation in python (Gaussian mixture probability hypothesis density filter)
A Gaussian-Mixtures Probability Hypothesis Density (GM-PHD) filter for multitarget tracking in a bayesian framework
The Gaussian Mixture Probability Hypothesis Density filter, GMPHD python implementation
An Object Oriented MATLAB toolkit for (Multi) Target Tracking.
Probabilistic Hypothesis Density filter with family.
tfabbri / gmphd-py
Forked from danstowell/gmphdGM-PHD filter implementation in python (Gaussian mixture probability hypothesis density filter)
Julia Package for discrete-time linear Gaussian parametric filtering systems, namely KF, EKF, UKF, GM-PHD
g2o: A General Framework for Graph Optimization