Robotic Vision Scene Understanding Challenge

Overview

The Robotic Vision Scene Understanding Challenge evaluates how well a robotic vision system can understand the semantic and geometric aspects of its environment.

We are working towards presenting this new challenge in November 2018 during our workshop at IROS.

Key features of the challenge:

There will be two tasks for the Scene Understanding Challenge: Object-based Semantic Mapping, and Spot the Difference.

Task 1: Object-based Semantic Mapping

For this task we evaluate how well participants can build a map of the environment that contains all objects of interest. The evaluation metric rewards accurate pose, shape, and object semantics.

This task can be done in one of two modes (Active or Passive) and two Streams (with or without groundtruth camera pose). In Active mode, the user can control the robot’s motion to explore the environment. In Passive mode, the user has no control over the robot. In both modes, the user code has access to the data from the robot’s RGB-D camera.

Task 2: Spot the Difference

The goal of this task is to identify all objects that disappeared, appeared, or moved in an environment from one day to another. The robot can explore the environment on both days, but has to spot all the differences.

This task can be done in one of two modes (Active or Passive) and two Streams (with or without groundtruth camera pose). In Active mode, the user can control the robot’s motion to explore the environment. In Passive mode, the user has no control over the robot. In both modes, the user code has access to the data from the robot’s RGB-D camera.

Watch the videos below Some objects disappeared, some new objects appeared. Can you spot the differences between both days? Which objects are new, which are gone? Can you write an algorithm to solve this problem?

Day 1
Day 2

Organisers, Support, and Acknowledgements

Stay in touch and follow us on Twitter for news and announcements: @robVisChallenge.

Niko Sünderhauf
Queensland University of Technology
Feras Dayoub
Queensland University of Technology
David Hall
Queensland University of Technology
Haoyang Zhang
Queensland University of Technology
Ben Talbot
Queensland University of Technology
Suman Bista
Queensland University of Technology



The Robotic Vision Challenges organisers are with the Australian Centre for Robotic Vision at Queensland University of Technology (QUT) in Brisbane, Australia.