KEYFRAME-BASED VISUAL-INERTIAL SLAM USING NONLINEAR OPTIMIZATION

Abstract—The fusion of visual and inertial cues has become popular in robotics due to the complementary nature of the two sensing modalities. While most fusion strategies to date rely on filtering schemes, the visual robotics community has recently turned to non-linear optimization approaches for tasks such as visual Simultaneous Localization And Mapping (SLAM), following the discovery that this comes with significant advantages in quality of performance and computational complexity. Following this trend, we present a novel approach to tightly integrate visual measurements with readings from an Inertial Measurement Unit (IMU) in SLAM. An IMU error term is integrated with the landmark reprojection error in a fully probabilistic manner, resulting to a joint non-linear cost function to be optimized. Employing the powerful concept of ‘keyframes ’ we partially marginalize old states to maintain a bounded-sized optimization window, ensuring real-time operation. Comparing against both vision-only and loosely-coupled visual-inertial algorithms, our experiments confirm the benefits of tight fusion in terms of accuracy and robustness. I

Projected texture stereo

Abstract — Passive stereo vision is widely used as a range sensing technology in robots, but suffers from dropouts: areas of low texture where stereo matching fails. By supplementing a stereo system with a strong texture projector, dropouts can be eliminated or reduced. This paper develops a practical stereo projector system, first by finding good patterns to project in the ideal case, then by analyzing the effects of system blur and phase noise on these patterns, and finally by designing a compact projector that is capable of good performance out to 3m in indoor scenes. The system has been implemented and has excellent depth precision and resolution, especially in the range out to 1.5m. I

Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping

Instrumenting and collecting annotated visual grasping datasets to train modern machine learning algorithms can be extremely time-consuming and expensive. An appealing alternative is to use off-the-shelf simulators to render synthetic data for which ground-truth annotations are generated automatically. Unfortunately, models trained purely on simulated data often fail to generalize to the real world. We study how randomized simulated environments and domain adaptation methods can be extended to train a grasping system to grasp novel objects from raw monocular RGB images. We extensively evaluate our approaches with a total of more than 25,000 physical test grasps, studying a range of simulation conditions and domain adaptation methods, including a novel extension of pixel-level domain adaptation that we term the GraspGAN. We show that, by using synthetic data and domain adaptation, we are able to reduce the number of real-world samples needed to achieve a given level of performance by up to 50 times, using only randomly generated simulated objects. We also show that by using only unlabeled real-world data and our GraspGAN methodology, we obtain real-world grasping performance without any real-world labels that is similar to that achieved with 939,777 labeled real-world samples.Comment: 9 pages, 5 figures, 3 table