It is challenging to utilize the large scale point clouds of semi-dense SLAM for real-time surface reconstruction. In order to obtain meaningful surfaces and reduce the number of points used in surface reconstruction, we propose to simplify the point clouds generated by semi-dense SLAM using 3D line segments. Specifically, we present a novel incremental approach for real-time 3D line segments extraction. Our experimental results show that the 3D line segments generated by our method are highly accurate compared to other methods. We demonstrate that using the extracted 3D line segments greatly improves the quality of 3D surface compared to using the 3D points directly from SLAM systems.
People: Xuebin Qin, Shida He, Zichen Zhang, Masood Dehghan and Martin Jagersand.
Salient closed boundaries are typical common structures of many objects, such as object contours, cup and bin rims. These closed boundaries are hard to be tracked due to the lack of enough textures. In our project, we address the problem by prior shape constrained line segments and edge fragments perceptual grouping. Related techniques, such as edge fragments detection, tracking measure definition and graph based optimization are proposed to form real-time trackers.
People: Xuebin Qin, Shida He, Xiucheng Yang, Masood Dehghan, Qiming Qin and Martin Jagersand.
We developed a semi-automatic image and video annotation tool. This annotation tool replaces the polygons approximation of boundaries by one-pixel-width pixel chains which are smoother and more accurate. It defines objects as groups of one or multiple boundaries that means not only simple objects, which consist of one closed boundary, but also complex objects, such as objects with holes, objects split by occlusions, can be labeled and annotated easily.
People: Mennatullah Siam, Heba Mahgoub, Mohamed Zahran, Senthil Yogamani, Martin Jagersand, Ahmad El-Sallab
For autonomous driving, moving objects like vehicles and pedestrians are of critical importance as they primarily influence the maneuvering and braking of the car. Typically, they are detected by motion segmentation of dense optical flow augmented by a CNN based object detector for capturing semantics. In this paper, our aim is to jointly model motion and appearance cues in a single convolutional network. We propose a novel two-stream architecture for joint learning of object detection and motion segmentation. We designed three different flavors of our network to establish systematic comparison.
People: Mennatullah Siam, Sepehr Valipour, Martin Jagersand, Nilanjan Ray
Semantic segmentation has recently witnessed major progress, where fully convolutional neural networks have shown to perform well. However, most of the previous work focused on improving single image segmentation. To our knowledge, no prior work has made use of temporal video information in a recurrent network. In this paper, we introduce a novel approach to implicitly utilize temporal data in videos for online semantic segmentation. The method relies on a fully convolutional network that is embedded into a gated recurrent architecture. This design receives a sequence of consecutive video frames and outputs the segmentation of the last frame.
MTF is a modular, extensible and highly efficient open source framework for registration based tracking targeted at robotics applications. It is implemented entirely in C++ and is designed from the ground up to easily integrate with systems that support any of several major vision and robotics libraries including OpenCV, ROS, ViSP and Eigen.
People: Ankush Roy, Xi Zhang, Nina Wolleb, Camilo Perez Quintero, Martin Jagersand
Tracking human manipulation tasks is challenging. This benchmark contains 100 videos of ordinary human manipulation tasks (pouring cereal, drinking coffee, moving and reading rigid books and flexible letters etc). The videos are categorized w.r.t. task difficulty, speed of motion and light conditions...
People: Laura Petrich; Martin Jagersand
Human assistive robotics can help the elderly and those with disabilities with Activities of Daily Living (ADL). Robotics researchers approach this bottom-up publishing on methods for control of different types of movements. Health research on the other hand focuses on hospital clinical assessment and rehabilitation using the International Classification of Functioning (ICF), leaving arguably important differences between each domain. In particular, little is known quantitatively on what ADLs humans perform in their ordinary environment - at home, work etc. This information can guide robotics development and prioritize what technology to deploy for in-home assistive robotics. This study targets several large lifelogging databases, where we compute (i) ADL task frequency from long-term low sampling frequency video and Internet of Things (IoT) sensor data, and (ii) short term arm and hand movement data from 30 fps video data of domestic tasks. Robotics and health care have different terms and taxonomies for representing tasks and motions. From the quantitative ADL task and ICF motion data we derive and discuss a robotics-relevant taxonomy in attempts to ameliorate these taxonomic differences.
People: Azad Shademan; Alejandro Hernandez-Herdocia; David Lovi; Neil Birkbeckartin; Jagersand
We have a partnership with the Canadian Space Agency (CSA) to develop semi-autonomous supervised control in space telerobotics using uncalibrated vision for traking and modeling...
People: Mona Gridseth
A visual interface where the user can specify tasks for the robot to complete using visual servoing...
People: Alejandro Hernandez-Herdocia; Azad Shademan; Martin Jagersand
An advanced dual-arm mobile manipulator is prototyped for research in semi-autonomous teleoperation and supervisory control...
Humans teach each other manipulation tasks through gestures and pointing. We develop an HRI where the robot interprets spatial gestures using comuter vision and carries out manipulation tasks...
A variational formulation for a discriminative model in image segmentation...