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Packages your JAR, assets and a JVM for distribution on Windows, Linux and Mac OS X

CasADi is a symbolic framework for numeric optimization implementing automatic differentiation in forward and reverse modes on sparse matrix-valued computational graphs. It supports self-contained C-code generation and interfaces state-of-the-art codes such as SUNDIALS, IPOPT etc. It can be used from C++, Python, Matlab/Octave, Julia or Javascript

robot_localization is a package of nonlinear state estimation nodes. The package was developed by Charles River Analytics, Inc. Please ask questions on answers.ros.org.

An optimal trajectory planner considering distinctive topologies for mobile robots based on Timed-Elastic-Bands (ROS Package)

  • ros
  • mobile-robots
  • navigation
  • trajectory-optimization

The Point Processing Toolkit (pptk) is a Python package for visualizing and processing 2-d/3-d point clouds.

An Event-Driven, Asynchronous, Behavioral State Machine Library for ROS (Robotic Operating System) applications written in C++

  • state-machine
  • ros
  • ros-packages
  • navigation

This is a package for LiDARTag, described in paper: LiDARTag: A Real-Time Fiducial Tag System for Point Clouds

  • fiducial-markers
  • lidar
  • point-clouds
  • cassie

CloudPeek is a lightweight, cross-platform, single-header C++ point cloud viewer. It’s designed for simplicity and efficiency, requiring no heavy libraries like PCL or Open3D. Ideal for visualizing and interacting with 3D data from LiDAR, photogrammetry, or other datasets, CloudPeek delivers powerful, real-time exploration in a minimalistic package

R package for segmentation, registration and generating web-based brain atlases from microscope images

Support vector machines (SVMs) and related kernel-based learning algorithms are a well-known class of machine learning algorithms, for non-parametric classification and regression. liquidSVM is an implementation of SVMs whose key features are: fully integrated hyper-parameter selection, extreme speed on both small and large data sets, full flexibility for experts, and inclusion of a variety of different learning scenarios: multi-class classification, ROC, and Neyman-Pearson learning, and least-squares, quantile, and expectile regression.