Abstract

We present efficient measurement models for localization in a feature based EKF SLAM framework. Both points and segments form the features, points include corners formed by intersection of wall like segments. The point features are described by its coordinates, while the segment feature is represented by the angle made by the normal to the segment from the global origin with the abscissa called the normal angle or N-angle for short. The measurement equation involves measuring the distance and bearing to the point feature and only bearing to the line feature. The distance measurement to the segment is intentionally kept out of the measurement equation due to its inefficacy in correcting the robot and landmarks state. This arises due to very large differences in the predicted and observed distances even for modest measurement errors when the robot is not very near the segment. Hence for this reason the segment feature is represented only by its N-angle devoid of distance since such a representation results in better state correction. The number of computations resulting from the covariance matrix updates is also less than a representation that includes both N-distance and N-angle.

Abstract

We present a method for extracting ground and other planes from a single non rotating laser mounted on a slow moving car used for on-road driving. A laser scan is decomposed into linear clusters. Corresponding clusters from subsequent scans are merged to form planes. The ground plane is identified based on the current vehicle height and the variance in height of the planes. Once these seed planes are identified future scan points either get associated with these planes or result in formation of new planes. Scan points that do not belong to any of the plane are left as such in the representation. Since the robustness of the method is contingent on how a single scan is decomposed into linear clusters, we compare the quality of the terrain representation due to three such clustering methods, one by iterative end point fit, other by adaptive breakpoint detection and thirdly the current method based on adaptive cosine similarity.

Abstract

We present a fast multi-robotic exploration methodology for 2D and 3D terrains. An asynchronous exploration strategy is introduced which shows significant improvements over the existing synchronous ones. A per-time visibility metric is being utilized by the algorithm. The metric allots the same weight for points for next view whose visibility over time ratios are equal. The outcome of this is that while the number of points visited to explore a terrain is nearly the same as other popular metrics found in literature, the time length of the paths are smaller in this case resulting in reduced time exploration. The results have been verified through extensive simulations in 2D and 3D. In 2D multiple robots explore unknown terrains that are office like, cluttered, corridor like and various combinations of these. In 3D we consider the case of multiple UAVs exploring a terrain represented as height fields. We introduce a way for calculating expected visibilities and a way of incorporating explored features in the per-time metric. The maximum height of the UAV at each location is governed by the so called exposure surface, beneath which the UAVs are constrained to fly. We also show performance gain of the present metric over others in experiments on a Pioneer 3DX robot.

Abstract

The objective of this work is to characterize certain important features of excitation of speech, namely, detecting the regions of glottal activity and estimating the strength of excita-tion in each glottal cycle. The proposed method is based on the assumption that the excitation to the vocal-tract system can be ap- proximated by a sequence of impulses of varying strengths. The effect due to an impulse in the time-domain is spread uniformly across the frequency-domain including at zero-frequency. We propose the use of a zero-frequency resonator to extract the characteristics of excitation source from speech signals by filtering out most of the time-varying vocal-tract information. The regions of glottal activity and the strengths of excitation estimated from the speech signal are in close agreement with those observed from the simultaneously recorded electro-glotto-graph signals. The performance of the proposed glottal activity detection is evaluated under different noisy environments at varying levels of degradation.

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