Shraddha Chaudhary

Bio: Shraddha Chaudhary is an academic researcher from Indian Institute of Technology Delhi. The author has contributed to research in topics: Pose & Visual servoing. The author has an hindex of 2, co-authored 6 publications receiving 14 citations. Previous affiliations of Shraddha Chaudhary include Indian Institutes of Technology.

Pose estimation of texture-less cylindrical objects in bin picking using sensor fusion

Abstract: We propose an approach for emptying of bin using a combination of Image and Range sensor. Offering a complete solution: calibration, segmentation and pose estimation, along with approachability analysis for the estimated pose. The work is novel in the sense that the objects to be picked are featureless and uniformly black in colour, hence existing approaches are not directly applicable. A key point involves optimal utilization of range data acquired from the laser scanner for 3-D segmentation using localized geometric information. This information guides segmentation of the image for better object pose estimation, used for pick-and-drop. We analytically assure the approachability of the object to avoid collision of the manipulator with the bin. Disturbance of objects caused during pick up has been modelled, which allows pickup of multiple pellets based on information from a single range scan. This eliminates the necessity of repeated scanning and data conditioning. The proposed method offers high object detection rate and pose estimation accuracy. The innovative techniques aimed at reducing the average pickup time makes it suitable for robust industrial operation.

Video-based road traffic monitoring and prediction using dynamic Bayesian networks

Abstract: The varied road conditions, chaotic and unstructured traffic, lack of lane discipline and wide variety of vehicles in countries like India, Pakistan and so on pose a need for a novel traffic monitoring system. In this study, the authors propose a novel camera-based traffic monitoring and prediction scheme without identifying or tracking vehicles. Spatial interest points (SIPs) and spatio-temporal interest points (STIPs) are extracted from the video stream of road traffic. SIP represents the number of vehicles and STIP represents the number of moving vehicles. The distributions of these features are then classified using Gaussian mixture model. In the proposed method, they learn the road state pattern using dynamic Bayesian network and predict the future road traffic state within a specific time delay. The predicted road state information can be used for traffic planning. The proposed method is computationally light, yet very powerful and efficient. The algorithm is tested for different weather conditions as well. They have validated their algorithm using Synchro Studio simulator and got 95.7% as average accuracy and on real-time video we got an accuracy of 84%.

Automated monocular vision based system for picking textureless objects

Abstract: This paper proposes an Autonomous Machine Vision system which grasps a textureless object from a clutter in a single plane, rearranges it for proper placement and then places it using vision. It contributes to a unique vision-based pose estimation algorithm, collision free path planning and dynamic Change-Over algorithm for final placement.

Pose estimation of a tilted pellet using single view from robot mounted camera

Abstract: This paper proposes a novel approach for finding the orientation of the tilted pellet using a single view of the pellet. This is important for the automation of pick and place problem. A texture less isolated cylindrical object is used for the pick and place operation. In this approach the isolated pellets are segmented from the background, following which, its contour is estimated. Then based on the assumption that length and diameter of the pellet remains constant, the mathematical formulation of the pose estimation of the pellet is given, and using which the orientation of the tilted pellet is computed using just single view. This is then experimentally verified for the different orientations of the pellet and the results are within the acceptable levels of accuracy A brief overview of the error estimation has been included. This error in orientation of the pellet is due to the variance in the actual height of the pellet. Error Jacobian for the above inaccuracy was calculated, and was found to be within required limits.

Vision for robotics

Abstract: Robot vision refers to the capability of a robot to visually perceive the environment and use this information for execution of various tasks. Visual feedback has been used extensively for robot navigation and obstacle avoidance. In the recent years, there are also examples that include interaction with people and manipulation of objects. In this paper, we review some of the work that goes beyond of using artificial landmarks and fiducial markers for the purpose of implementing vision-based control in robots. We discuss different application areas, both from the systems perspective and individual problems such as object tracking and recognition.

Vehicular Traffic Congestion Classification by Visual Features and Deep Learning Approaches: A Comparison.

Abstract: Automatic traffic flow classification is useful to reveal road congestions and accidents. Nowadays, roads and highways are equipped with a huge amount of surveillance cameras, which can be used for real-time vehicle identification, and thus providing traffic flow estimation. This research provides a comparative analysis of state-of-the-art object detectors, visual features, and classification models useful to implement traffic state estimations. More specifically, three different object detectors are compared to identify vehicles. Four machine learning techniques are successively employed to explore five visual features for classification aims. These classic machine learning approaches are compared with the deep learning techniques. This research demonstrates that, when methods and resources are properly implemented and tested, results are very encouraging for both methods, but the deep learning method is the most accurately performing one reaching an accuracy of 99.9% for binary traffic state classification and 98.6% for multiclass classification.

Combination of geometric and parametric approaches for kinematic identification of an industrial robot

Abstract: Combination of geometric and parametric approaches of kinematic identification is proposed in this article. The experimental strategy is similar to that used in geometric approach wherein each axis of the robot is actuated one after the other. This adds clarity to experimental strategy, which becomes ambiguous while solely using a conventional parametric approach. Therefore it is easier to conduct experiments even if there are restrictions in workspace. The estimation was done using a parametric technique, but in a stage wise manner using a divide and conquer strategy. This allowed measurement of the robot accuracy after removing the errors arising due to the definition of base and end-effector frames. Additionally it is possible to visualize the reduction in errors during the estimation process. In addition to this, the Jacobian matrix that relates the pose errors to the correction in parameters is adapted during estimation using a damped least squares method depending upon the convergence of the parameters. Results were obtained after extensive experiments on industrial robots using three different measurement instruments namely laser tracker, monocular camera and multi-camera system. The proposed method performs better than the conventional approach which uses only geometric approach. Finally thanks to the new approach, it was possible to conduct experiments after dividing the workspace region into those with high and low levels of observability; which is not easy while using conventional approaches. It was also possible to perform identification in regions closer and farther away from the robot where there is deterioration of observability. The results show that the proposed method could reduce the errors in pose in a consistent manner, even when different measurement instruments were used, or when there was a deterioration in observability due to the choice of poses during identification.

Driving behavior analysis of city buses based on real-time GNSS traces and road information

Abstract: The driving behavior of bus drivers is related to the safety of all passengers and regulation of urban traffic. In order to analyze the relevant characteristics of speed and acceleration, accurate bus trajectories and patterns are essential for driver behavior analysis and development of effective intelligent public transportation. Exploiting real-time vehicle tracking, this paper develops a platform with vehicle-mounted terminals using differential global navigation satellite system (DGNSS) modules for driver behavior analysis. The DGNSS traces were used to derive the vehicle trajectories, which were then linked to road information to produce speed and acceleration matrices. Comprehensive field tests were undertaken on multiple bus routes in urban environments. The spatiotemporal results indicate that the platform can automatically and accurately extract the driving behavior characteristics. Furthermore, the platform's visual function can be used to effectively monitor driving risks, such as speeding and fierce acceleration, in multiple bus routes. The details of the platform's features are provided for intelligent transport system (ITS) design and applications.