SegICP: Integrated Deep Semantic Segmentation and Pose Estimation

Recent robotic manipulation competitions have highlighted that sophisticated robots still struggle to achieve fast and reliable perception of task-relevant objects in complex, realistic scenarios. To improve these systems' perceptive speed and robustness, we present SegICP, a novel integrated solution to object recognition and pose estimation. SegICP couples convolutional neural networks and multi-hypothesis point cloud registration to achieve both robust pixel-wise semantic segmentation as well as accurate and real-time 6-DOF pose estimation for relevant objects. Our architecture achieves 1cm position error and <5^\circ$ angle error in real time without an initial seed. We evaluate and benchmark SegICP against an annotated dataset generated by motion capture.Comment: IROS camera-read

What do we perceive in a glance of a real-world scene?

What do we see when we glance at a natural scene and how does it change as the glance becomes longer? We asked naive subjects to report in a free-form format what they saw when looking at briefly presented real-life photographs. Our subjects received no specific information as to the content of each stimulus. Thus, our paradigm differs from previous studies where subjects were cued before a picture was presented and/or were probed with multiple-choice questions. In the first stage, 90 novel grayscale photographs were foveally shown to a group of 22 native-English-speaking subjects. The presentation time was chosen at random from a set of seven possible times (from 27 to 500 ms). A perceptual mask followed each photograph immediately. After each presentation, subjects reported what they had just seen as completely and truthfully as possible. In the second stage, another group of naive individuals was instructed to score each of the descriptions produced by the subjects in the first stage. Individual scores were assigned to more than a hundred different attributes. We show that within a single glance, much object- and scene-level information is perceived by human subjects. The richness of our perception, though, seems asymmetrical. Subjects tend to have a propensity toward perceiving natural scenes as being outdoor rather than indoor. The reporting of sensory- or feature-level information of a scene (such as shading and shape) consistently precedes the reporting of the semantic-level information. But once subjects recognize more semantic-level components of a scene, there is little evidence suggesting any bias toward either scene-level or object-level recognition