Implicit 3D Orientation Learning for 6D Object Detection from RGB Images

We propose a real-time RGB-based pipeline for object detection and 6D pose estimation. Our novel 3D orientation estimation is based on a variant of the Denoising Autoencoder that is trained on simulated views of a 3D model using Domain Randomization. This so-called Augmented Autoencoder has several advantages over existing methods: It does not require real, pose-annotated training data, generalizes to various test sensors and inherently handles object and view symmetries. Instead of learning an explicit mapping from input images to object poses, it provides an implicit representation of object orientations defined by samples in a latent space. Our pipeline achieves state-of-the-art performance on the T-LESS dataset both in the RGB and RGB-D domain. We also evaluate on the LineMOD dataset where we can compete with other synthetically trained approaches. We further increase performance by correcting 3D orientation estimates to account for perspective errors when the object deviates from the image center and show extended results.Comment: Code available at: https://github.com/DLR-RM/AugmentedAutoencode

A Simulation Environment with Reduced Reality Gap for Testing Autonomous Vehicles

In order to facilitate acceptance and ensure safety, autonomous vehicles must be tested not only in typical and relatively safe scenarios but also in dangerous and less frequent scenarios. Recent pedestrian fatalities caused by test vehicles of the front-running giants like Google and Tesla suffice the fact that Autonomous Vehicle technology is not yet mature enough and still needs rigorous exposure to a wide range of traffic, landscape, and natural conditions on which the Autonomous Vehicles can be trained on to perform as expected in real traffic conditions. Simulation Environments have been considered as an efficient, safe, flexible and cost-effective option for the training, testing, and validation of Autonomous Vehicle technology. While ad-hoc task-specific use of simulation in Autonomous Driving research is widespread, simulation platforms that bridge the gap between simulation and reality are limited. This research proposes to set up a highly realistic simulation environment (using CARLA driving simulator) to generate realistic data to be used for Autonomous Driving research. Our system is able to recreate the original traffic scenarios based on prior information about the traffic scene. Furthermore, the system will allow to make changes to the original scenarios and create various desired testing scenarios by varying the parameters of traffic actors, such as location, trajectory, speed, motion states, etc. and hence collect more data with ease