Abstract

Global localization algorithms involve a search over all possible poses of the robot that can be typically over a large space in huge maps. Essentially it involves computing a posterior by seeing how probable are the obtained sensor readings at each of the discretized states in a map. Instead in this paper by reverse projecting the sensor readings from the obstacle boundaries onto the surroundings, a solution is obtained by searching over the space of obstacle boundaries than by a search in the discretized pose space. That this search over obstacle boundaries is considerably less if the ratio of free space to boundary space in a map is high is straightforward. However we also show theoretically that even when the boundary space exceeds the free space the computations due to the current method does not exceed those due to the popular Markov and Correlation based approaches to global localization. The comparative advantages are well documented in simulation section of the paper. The approach is able to consistently localize a laser equipped robot in our lab.

Abstract

We present a method for coordinating multi-robotic/multi-agent traffic control at intersections. The robotic agents (RA) move guided by a potential field along the lanes. At the intersections an intersection agent (IA) controls the flow of traffic by assigning priorities to the agents that are about to enter the intersection. The priorities are computed based on the density of RA in a lane and the flow rate of traffic in those lanes. The RAs integrate these assigned priorities into their potential field computations. The modified potential field computations help the RAs to move through the intersection avoiding collisions. An elegant mixed autonomy scheme is thereby achieved where the IAs decide upon the priorities at the intersection while the low level collision avoidance maneuvers are left with the individual RAs. This scheme preserves the distributed nature and the autonomy of potential field maneuvers while simultaneously balancing the computation load between the IA and RAs. We compare this method with a method where the RAs navigate the intersection without a superior direction from the IAs through priorities or when IAs direct the RAs based on priorities computed on a first come first served basis. We show performance gain over both these methods in simulations.

Abstract

Secure multiparty protocols have found applications in numerous domains, where multiple nontrusting parties wish to evaluate a function of their private inputs. In this paper, we consider the case of multiple robots wishing to localize themselves, with maps as their private inputs. Though localization of robots has been a well studied problem, only recent studies have shown how to actively localize multiple robots through coordination. In all such studies, localization has typically been achieved through constructing a publicly known global map. Here, we show how a similar solution can be given in the case of nontrusting robots, which do not wish to disclose their local maps.

Abstract

In this paper we describe the robot software development framework with database at its core. In this framework the features of database is harnessed to make various components modular, and robot data handling and processing is made much more flexible. This empowers the researchers to design the system quickly, access the data and allow multiple collaborators work to be integrated with ease. The advantage of database based framework is explained case by case for various scenarios with example robot application developments.

Abstract

Widespread use of surveillance cameras in offices and other business establishments, pose a significant threat to the privacy of the employees and visitors. The challenge of introducing privacy and security in such a practical surveillance system has been stifled by the enormous computational and communication overhead required by the solutions. In this paper, we propose an efficient framework to carry out privacy preserving surveillance. We split each frame into a set of random images. Each image by itself does not convey any meaningful information about the original frame, while collectively, they retain all the information. Our solution is derived from a secret sharing scheme based on the Chinese Remainder Theorem, suitably adapted to image data. Our method enables distributed secure processing and storage, while retaining the ability to reconstruct the original data in case of a legal requirement. The system installed in an office like environment can effectively detect and track people, or solve similar surveillance tasks. Our proposed paradigm is highly efficient compared to Secure Multiparty Computation, making privacy preserving surveillance, practical.

Abstract

Writer independent handwriting recognition systems are limited in their accuracy, primarily due the large variations in writing styles of most characters. Samples from a single character class can be thought of as emanating from multiple sources, corresponding to each writing style. This also makes the inter-class boundaries, complex and disconnected in the feature space. Multiple kernel methods have emerged as a potential framework to model such decision boundaries effectively, which can be coupled with maximal margin learning algorithms. We show that formulating the problem in the above framework improves the recognition accuracy. We also propose a mechanism to adapt the resulting classifier by modifying the weights of the support vectors as well as that of the individual kernels. Experimental results are presented on a data set of 16,000 alphabets collected from 470 writers using a digitizing tablet

Abstract

Search and retrieval is gaining importance in the ink domain due to the increase in the availability of online handwritten data. However, the problem is challenging due to variations in handwriting between various writers, digitizers and writing conditions. In this paper, we propose a retrieval mechanism for online handwriting, which can handle different writing styles, specifically for Indian languages. The proposed approach provides a keyboard-based search interface that enables to search handwritten data from any platform, in addition to pen-based and example-based queries. One of the major advantages of this framework is that information retrieval techniques such as ranking relevance, detecting stopwords and controlling word forms are extended to work with search and retrieval in the ink domain. The framework also allows cross-lingual document retrieval across Indian languages

Abstract

We propose an approach to restore severely degraded document images using a probabilistic context model. Unlike traditional approaches that use previously learned prior models to restore an image, we are able to learn the text model from the degraded document itself, making the approach independent of script, font, style, etc. We model the contextual relationship using an MRF. The ability to work with larger patch sizes allows us to deal with severe degradations including cuts, blobs, merges and vandalized documents. Our approach can also integrate document restoration and super-resolution into a single framework, thus directly generating high quality images from degraded documents. Experimental results show significant improvement in image quality on document images collected from various sources including magazines and books, and comprehensively demonstrate the robustness and adaptability of the approach. It works well with document collections such as books, even with severe degradations, and hence is ideally suited for repositories such as digital libraries

Abstract

Biometric authentication over public networks leads to a variety of privacy issues that needs to be addressed before it can become popular. The primary concerns are that the biometrics might reveal more information than the identity itself, as well as provide the ability to track users over an extended period of time. In this paper, we propose an authentication protocol that alleviates these concerns. The protocol takes care of user privacy, template protection and trust issues in biometric authentication systems. The protocol uses asymmetric encryption, and captures the advantages of biometric authentication. The protocol provides non-repudiable identity verification, while not revealing any additional information about the user to the server or vice versa. We show that the protocol is secure under various attacks. Experimental results indicate that the overall method is efficient to be used in practical scenarios.

Abstract

A palmprint based authentication system that can work with a multipurpose camera in uncontrolled circumstances, such as those mounted on a laptop, mobile device or those for surveillance, can dramatically increase the applicability of such a system. However, the performance of existing techniques for palmprint authentication fall considerably, when the camera is not aligned with the surface of the palm. The problems arise primarily due to variations in appearance introduced due to varying pose, but is compounded by specularity of the kin and blur due to motion and focus. In this paper, we propose a method to deal with variations in pose in unconstrained palmprint imaging. The method can robustly estimate and correct variations in pose, and compute a similarity measure between the corrected test image and a reference image. Experimental results on a set of 100 user’s palms captured at varying poses show a reduction in Equal Error Eate from 22.4% to 8.7%.