ACRV Scene Understanding Challenge
This novel challenge tasked competitors with creating systems that can understand the semantic and geometric aspects of an environment through two distinct tasks: Object-based Semantic SLAM and Scene Change Detection. The challenge provided high-fidelity, simulated environments for testing, three challenge difficulties, a simple AI Gym-style API enabling sim-to-real transfer, and a new evaluation measure for evaluating semantic object maps. All of this was enabled using the newly-created BenchBot framework also developed here at the ACRV.
Probabilistic Object Detection (PrOD) Challenge
If a robot moves with overconfidence about its surroundings … it is going to break things.
In our probabilistic object detection (PrOD) challenge, we needed our object detection systems to move beyond a simple bounding box and class label score. This challenge requires participants to detect objects in video data produced from high-fidelity simulations.
The novelty of this challenge is that participants are rewarded for providing accurate estimates of both spatial and semantic uncertainty for every detection using probabilistic bounding boxes.
To find out more about the ACRV robotic vision challenges, check out our website: roboticvisionchallenge.org