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Commit 5cb127a3 authored by Shengkang (William) Chen's avatar Shengkang (William) Chen
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## Synopsis ## Synopsis
Cooperative localization is still a challenging task for cooperative robot control. CoLo is a performance evaluation system for two-dimensional cooperative localization algorithms. The system consists of a physical experiment (CoLo-PE) for data collection and a software analysis tool (CoLo-AT) using real-world datasets to evaluate the performances of users’ cooperative localization algorithms. The goal of this project to let users to create and evaluate their own algorithms with some existing algorithms. Cooperative localization is still a challenging task for cooperative robot control. CoLo is a performance evaluation system for two-dimensional cooperative localization algorithms. The system consists of a physical experiment (CoLo-PE) for data collection and a software analysis tool (CoLo-AT) using real-world datasets to evaluate the performances of users’ cooperative localization algorithms. The goal of this project is to let users create and evaluate their own algorithms with some existing algorithms.
[Project Website](https://uclalemur.com/research/colo-simulation-environment-for-cooperative-localization) [Project Website](https://uclalemur.com/research/colo-simulation-environment-for-cooperative-localization)
## Structure ## Structure
There are two main parts in CoLo: a physical experiment (CoLo-PE) for data collection and a software analysis tool (CoLo-AT) There are two main parts in CoLo: a physical experiment (CoLo-PE) for data collection and a software analysis tool (CoLo-AT)
In CoLo-PE, it will shows the requriment hardwares and softwares needed for setting up the the physical experiments. In CoLo-PE, it will show the required hardware and software needed for setting up the physical experiments.
In CoLo-AT, users can load their localization algorithms and test their performances using different datasets on various settings. In CoLo-AT, users can load their localization algorithms and test their performances using different datasets in various settings.
Each parts can be used independetly for users' needs. Each part can be used independently for users' needs.
## Cite this work ## Cite this work
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