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stefangachter's Projects

smile icon smile

Statistical Machine Intelligence & Learning Engine

sophus icon sophus

C++ implementation of Lie Groups using Eigen.

spatialmath-python icon spatialmath-python

Create, manipulate and convert representations of position and orientation in 2D or 3D using Python

symforce icon symforce

Fast symbolic computation, code generation, and nonlinear optimization for robotics

synimu icon synimu

This package implements a non-parametric method that makes use of the wavelet cross-covariance at different scales to combine the measurements coming from an array of sensors in order to deliver an optimal measurement signal without needing any assumption on the processes underlying the individual error signals.

tic-tac-toe-reinforcement-learning icon tic-tac-toe-reinforcement-learning

Agent learns to play Tic-Tac-Toe using reinforcement learning. The agent was trained by playing against itself. Human can also play against trained Agent.

tlio icon tlio

Tight Learned Inertial Odometry

ts icon ts

:notebook: "An Introduction to Time Series Analysis with R" is a text which is currently under development and aims at giving readers a general overview of the main aspects that characterise time series and the most common tools used for their analysis.

ugpm icon ugpm

Continuous preintegration

ukfm icon ukfm

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

vi_sensor_sdk icon vi_sensor_sdk

Loitor VI Sensor SDK, ROS pack and IMU calibration program.

vins-mobile icon vins-mobile

Monocular Visual-Inertial State Estimator on Mobile Phones

vins-mono icon vins-mono

A Robust and Versatile Monocular Visual-Inertial State Estimator

wave_geometry icon wave_geometry

Manifold geometry with fast automatic derivatives and coordinate frame semantics checking

wgs_conversions icon wgs_conversions

A ROS server implementation of the World Geodetic System (WGS) conversion functions for converting to/from LLA, ENU, and ECEF.

wv icon wv

:alarm_clock: This R package provides the tools to perform standard and robust wavelet variance analysis for time series (signal processing). Among others, aside from computing the wavelet variance and cross-covariance (classic and robust), the package provides inference tools (e.g. confidence intervals) and plotting tools allowing to perform some visual analysis and assess the properties of the underlying time series.

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