We’re working hard on developing useful open source robotics software for the community! Check our GitHub for the most recent updates. The packages below are general contributions built by our laboratory; if you’re looking for code that is specific to a certain publication or dataset, please head over to the appropriate page.
Our open source tail-sitter platform, now called the ‘Phoenix’. This repository contains a complete package of design documents, CAD models, simulation tools, and onboard firmware code necessary to build, assemble, fly and experiment with a novel tail-sitter aerial vehicle!
Training and test files for a Bayesian Convolutional Neural Network (implemented in Caffe) that can infer sun direction to within 10 degrees on most images. Preprocessed data and ground truth sun directions for the KITTI odometry benchmarks are also provided.
This package provides a minimal set of tools for working with the KITTI dataset in Python.
Importing KITTI data is as easy as:
data = pykitti.raw('/your/dataset/dir', '2011_09_26', '0019').
Supporting code for Lee Clement’s and Valentin Peretroukhin’s CRV paper: The Battle for Filter Supremacy: A Comparative Study of the Multi-State Constraint Kalman Filter and the Sliding Window Filter.