Photo: Prof Madhava Krishna with his students at the Robotics Research Centre IIIT, Hyderabad
Hyderabad, May 9 2017: Students of the International Institute of Information Technology ( IIIT) Hyderabad, will present their latest research in robotics at the premier international forum for robotics researchers -- International Conference on Robotics and Automation (ICRA) -- in Singapore from May 29 to June 3, 2017.
The conference encourages innovative R&D talent, dynamic and goal-driven entrepreneurs and practitioners using robotics and automation technology to solve challenging real-world problems.
Under the guidance of Associate Professor K Madhava Krishna, students at IIIT-Hyderabad are encouraged to explore robotics through various aspects – deep learning, computer vision or even artificial intelligence – and apply robotic vision to mobile robots and drones.
In keeping with the conference’s theme of “Innovation, Entrepreneurship, and Real-world Solutions”, IIIT-Hyderabad’s Robotics Research Centre, will be presenting these papers:
Detachable Modular Robot capable of Cooperative Climbing and Multi Agent Exploration by Sri Harsha Turlapati, Ankur Srivastava, K. Madhava Krishna and Suril V. Shah
Imagine a disaster diminution situation, or a search and rescue mission where you need to be able to enter various arenas (most possibly dangerous ones) in the most agile way possible and without being detected. Fulfilling these specifications, students at the Robotics Research have built a Detachable Compliant Modular Robot (DCMR) prototype that performs concurrent scene exploration by detaching into numerous parts and reattach itself to climb stairs of almost any type in urban settings. The DCMR has been built using both the Uneven Terrain Navigation and Multi Agent Systems (MAS) specifications. The robot comprises a spring that can climb all sorts of stairs, and an extra set of actuators per module to enable the detachment and re-attachment of the modules. These DCMRs can also move through crammed spaces by detaching itself from one another. Robots normally need to have an active perception of their surroundings in order to move, but these are optimized to handle various terrains at the same time.
Exploring Convolutional Networks for End-to-End Visual Servoing by Aseem Saxena, Harit Pandya, Gourav Kumar and K. Madhava Krishna
An end-to-end learning based approach for visual servoing in diverse scenes where the knowledge of camera parameters and scene geometry is not available previously. They achieved this by training a convolutional neural network over colour images with synchronised camera poses. They also demonstrated the efficacy and robustness of the approach for a wide range of camera poses in both indoor as well as outdoor environments and using experiments performed in simulation and using a quadrotor.
Reconstructing Vechicles from a Single Image: Shape Priors for Road Scene Understanding by J. Krishna Murthy, G.V. Sai Krishna, Falak Chhaya and K. Madhava Krishna
With automatic and autonomous driving emerging as the latest in robotic study, the robot vision community has been realising the importance of understanding road scenes; many of these extensively using LiDAR or stereo camera rigs. These however are very expensive, given the extensive systems in use. And an increasing interest is being shown to replace these expensive systems with cheaper off-the-shelf cameras.
Students at IIIT Hyderabad have taken this interesting challenge and solved the specific problem of recovering 3D shapes and pose of vehicles, given a single (RGB) image. The basic premise used has been the fact that human beings have fine visual perceptive ability that can recognize external information and perceive the 3D structure from a single image thanks to vast prior knowledge on how various 3D shapes project in 2D.|
The students have placed similar capabilities in the machines they have built, by learning the shapes, and using them along with other constraints arising from projective imaging geometry in a robust optimization framework. The publication also comprises a novel shape-aware adjustment scheme which estimates the 3D pose and shape of a vehicle, given the shape prior and keypoint detections.