NaveGo: an open-source MATLAB/GNU Octave toolbox for processing integrated navigation systems and performing inertial sensors analysis.
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Updated
Feb 24, 2024 - MATLAB
NaveGo: an open-source MATLAB/GNU Octave toolbox for processing integrated navigation systems and performing inertial sensors analysis.
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.
LiDAR-Inertial 3D Plane Simulator
A Laser-Camera Calibration Toolbox extending from that at http://www.cs.cmu.edu/~ranjith/lcct.html
interfaces and algorithms for event based cameras, lidars, and actuators
Probabilistic line extraction from 2-D range scan
A collection of digital forestry tools for Matlab/Octave
Given a map data (image + lidar), estimate the 6 DoF camera pose of the query image.
Object detection and transfer learning on point clouds using pretrained Complex-YOLOv4 models in MATLAB
MATLAB code for LiDAR-Camera-GNSS/INS extrinsic calibration based on hand-eye calibration method.
Information and resources about bathymetric data from NASA's ICESat-2 mission, created by the ICESat-2 Science Team Bathymetry Working Group.
Code and documents to support the Thesis: Progress Towards LiDAR Based Bicycle Detection in Urban Environments Edit Add topics
LiDAR data processing, object recognition from point clouds, and LiDAR remote sensing.
Semantic segmentation and transfer learning using pretrained SalsaNext model in MATLAB
polarPcolor draws a pseudocolor plot in polar coordinates with a polar grid.
Prototype implementation of a non-rigid point cloud registration algorithm using piece-wise tricubic polynomials as transformation model.
Testing of transformation matrices between Lidar Velodyne VLP16 and RGB camera with MatLAB
This package introduces the concept of optimizing target shape to remove pose ambiguity for LiDAR point clouds. Both the simulation and the experimental results confirm that by using the optimal shape and the global solver, we achieve centimeter error in translation and a few degrees in rotation even when a partially illuminated target is placed…
This package introduces the concept of optimizing target shape to remove pose ambiguity for LiDAR point clouds. Both the simulation and the experimental results confirm that by using the optimal shape and the global solver, we achieve centimeter error in translation and a few degrees in rotation even when a partially illuminated target is placed…
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