Calico is a lightweight visual-inertial calibration library that allows for rapid problem construction, debugging, and tool creation. Unlike other codebases that are limited to standalone calibration tools for specific hardware setups, Calico is a flexible library that enables building and modifying custom tools to suit your hardware requirements. If your calibration problem aligns with our geometry paradigm, there is no need to modify the underlying optimization.
Calico features:
- Flexible auto-differentiable cost functors.
- Sensor intrinsics, extrinsics, and latency estimation.
- The ability to add multiple sensors.
- Measurement outlier tagging and exclusion.
- Per-sensor robustifier kernels.
- Ability to create custom sensor models.
Check out our wiki pages for more info.
- The minimum supported version of Ubuntu is 22.04.
-
- This is because Ubuntu 20.04 and older do not ship with libabsl-dev.
- The version of Ceres that ships with 22.04 is too old for Calico, run
./scripts/install-ceres.shto install the latest version. -
- Optional: Install the latest version of the CUDA sdk before you install Ceres
You will also need the following dependencies:
sudo apt install -y libeigen3-dev libgtest-dev libabsl-dev \
libopencv-dev libyaml-cpp-dev libgmock-dev
mkdir build
cd build
cmake ..
make -j3
make installFrom your host container run:
./scripts/build-python.sh
The self-contained wheel will be saved into the wheelhouse directory.
The wheel wraps the native source in the module calico.calico_native and the python utils in calico.utils.
Poor sensor calibration is a problem endemic to robotics, yet it's typically not given enough attention because it tends to detract from higher level project goals. The purpose of this library is to help roboticists quickly resolve their calibration issues so that they can move on to more interesting things. This library is hereby granted the MIT license, to be used free of charge by anyone within academia or industry.
To the best of my knowledge, all library dependencies also fall under BSD 2/3, LGPL, or Apache 2.0.
Copyright (c) 2023 James Yang
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