Leveraging (Contradictory) Prior Data

Many industrial equipment/tools/robotics today are constrained to very rigid automation regimes. The following applications will be disrupted by navigation-affordance technology:

  • * Reconfiguring manufacturing automation such as picking or packing using both asynchronous measurements and user desired programming,
  • * Enhanced automation in surveyor Total Stations for lowering operational costs on construction sites,
  • * Validation of "as-designed" vs. "as-built" in construction using design plans as affordances,
  • * Metrology in manufacturing verification of part dimensions based on "as-designed" 3D solid models,
  • * Rapid localization of underwater robotics in obscure environments using prior maps of semi-known environments,
    • * e.g. relative navigation to known subsea infrastructure in energy or aquaculture,
  • * Discrepancy detection through robotic inspection but detecting contradictions between intended and robotically mapped,
    • * e.g. kelp farming installation status check that growth lines are not broken,
  • * And many more.

The figure above shows a motivational example where prior knowledge---i.e. the corners of a rectangle and door opening---is to be used for robotic mapping operations. This simplified motivational example will show you how!

Choose your own adventure

Or clone the github repo here: https://github.com/NavAbility/BinderNotebooks.git

Your quest

Python

An excellent programming language. Very popular in data science, machine learning and robotics!

Zero Install

Jupyter notebooks running in a cloud environment. Click begin to jump over to the Binder environment!

NavAbility SDK

NavAbility's fully featured, multi-language implementation of Caesar.jl. Your example will run in NavAbility Cloud. The SDK versions of the tutorials are coming soon.