Browsing by Author "Kotha, Bhavi Bharat"
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- Autonomous Vehicle Waypoint Navigation Using Hyper-ClothoidsKotha, Bhavi Bharat (Virginia Tech, 2022-01-20)This research study presents two control solutions, PID and the novel hyper-clothoid control strategy, to autonomously navigate a car. These waypoint navigation solutions smoothly connect the given waypoints with C1 continuity using Hermite cubic splines which is used as a reference path for the controller to track. The PID controller uses lateral and heading error to generate a steering profile for the vehicle to track the constructed reference path. A novel real time solution is presented as the second control strategy which involves constructing clothoids to generate a steering profile. The resultant car trajectory preserves curvature and curvature rate continuity. A simulation test bench was developed in MATLAB and Simulink. Six benchmark waypoint datasets have been used for regression testing and validating the algorithms. Both the proposed control strategies have been implemented on a 2017 GM Chevy Bolt EV. A real time operating system (QNX) has been used and was time-synced with the localization suite in the test vehicle. Closed loop results with accuracies up to 50 cm of lateral error have been achieved using the test vehicle.
- Using Texture Features To Perform Depth EstimationKotha, Bhavi Bharat (Virginia Tech, 2018-01-22)There is a great need in real world applications for estimating depth through electronic means without human intervention. There are many methods in the field which help in autonomously finding depth measurements. Some of which are using LiDAR, Radar, etc. One of the most researched topic in the field of depth measurements is Computer Vision which uses techniques on 2D images to achieve the desired result. Out of the many 3D vision techniques used, stereovision is a field where a lot of research is being done to solve this kind of problem. Human vision plays an important part behind the inspiration and research performed in this field. Stereovision gives a very high spatial resolution of depth estimates which is used for obstacle avoidance, path planning, object recognition, etc. Stereovision makes use of two images in the image pair. These images are taken with two cameras from different views and those two images are processed to get depth information. Processing stereo images has been one of the most intensively sought-after research topics in computer vision. Many factors affect the performance of this approach like computational efficiency, depth discontinuities, lighting changes, correspondence and correlation, electronic noise, etc. An algorithm is proposed which uses texture features obtained using Laws Energy Masks and multi-block approach to perform correspondence matching between stereo pair of images with high baseline. This is followed by forming disparity maps to get the relative depth of pixels in the image. An analysis is also made between this approach to the current state-of-the-art algorithms. A robust method to score and rank the stereo algorithms is also proposed. This approach provides a simple way for researchers to rank the algorithms according to their application needs.