[visionlist] [meetings] Call for Participants - ACRV Robotic Vision Scene Understanding Challenge
d20.hall at qut.edu.au
Thu Sep 3 02:27:15 -04 2020
Call for Participants - ACRV Robotic Vision Scene Understanding Challenge
This is a call for participants for the latest ACRV robotic vision challenge on scene understanding.
Eval AI Challenge Link: https://evalai.cloudcv.org/web/challenges/challenge-page/625/overview
Challenge Overview Webpage: https://nikosuenderhauf.github.io/roboticvisionchallenges/scene-understanding
Deadline: The deadline for the challenge has been extended to October 2nd.
Prizes: 1 Titan RTX and up to 5 Jetson Nano GPUs for two winning teams provided by NVIDIA + $5,000 USD to be divided amongst high-performing competitors provided by the ACRV
The Robotic Vision Scene Understanding Challenge evaluates how well a robotic vision system can understand the semantic and geometric aspects of its environment. The challenge consists of two distinct tasks: Object-based Semantic SLAM, and Scene Change Detection.
Key features of this challenge include:
* BenchBot, a complete software stack for running semantic scene understanding algorithms.
* Running algorithms in realistic 3D simulation, and on real robots, with only a few lines of Python code.
* Tiered difficulty levels to allow for easy of entry to robotic vision with embodied agents and enabling ablation studies.
* The BenchBot API, which allows simple interfacing with robots and supports OpenAI Gym-style approaches and a simple object-oriented Agent approach.
* Easy-to-use scripts for running simulated environments, executing code on a simulated robot, evaluating semantic scene understanding results, and automating code execution across multiple environments.
* Opportunities for the best teams to execute their code on a real robot in our lab, which uses the same API as the simulated robot.
* Use of the Nvidia Isaac SDK for interfacing with, and simulation of, high fidelity 3D environments.
Object-based Semantic SLAM: Participants use a robot to traverse around the environment, building up an object-based semantic map from the robot’s RGBD sensor observations and odometry measurements.
Scene Change Detection: Participants use a robot to traverse through an environment scene, building up a semantic understanding of the scene. Then the robot is moved to a new start position in the same environment, but with different conditions. Along with a possible change from day to night, the new scene has a number objects added and / or removed. Participants must produce an object-based semantic map describing the changes between the two scenes.
Difficulty Levels: We provide three difficulty levels of increasing complexity and similarity to true active robotic vision systems. At the simplest difficulty level, the robot moves to pre-defined poses to collect data and provides ground-truth poses, removing the need for active exploration and localization. The second level requires active exploration and robot control but still provides ground-truth pose to remove localization requirements. The final mode is the same as the previous but provides only noisy odometry information, requiring localization to be calculated by the system.
E-mail: contact at roboticvisionchallenge.org
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