Faculty Publications
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Publications by NITK Faculty
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Item An android GPS-based navigation application for blind(Association for Computing Machinery, 2014) Nisha, K.K.; Pruthvi, H.R.; Hadimani, S.N.; Guddeti, G.R.M.; Ashwin, T.S.; Domanal, S.G.Visual Impairment makes the person depend on another person for all his works and daily chores. Through the application proposed in this paper, we aim to eliminate this dependency of a visually impaired person when travelling from one place to another. The main goal is to provide information regarding the current location, how much distance and time is required to reach the destination as well as provide the user with the directions and turns to be taken while travelling by providing continuous audio feedback in his understandable language. © is held by the author/owner(s).Item A Framework To Study Heuristic TSP Algorithms With Google Maps API(Institute of Electrical and Electronics Engineers Inc., 2019) Ajumal, P.A.; Ananthakrishnan, S.; Jain, A.; Athreya, H.N.; Chandrasekaran, K.Millions of people depend on the navigation facilities available in smart-phones and web browsers for their daily commutes, planning long trips ahead of time, looking up places etc. Integration of GPS and compass made navigating anywhere in the world a trivial task. Today, there are several applications available that fit the purpose of navigation such as Waze, HereWeGo (previously known as Here Maps by Nokia), Google Maps, etc. When Google Maps was used to embark on a tour that will take us to chosen places by covering the least distance possible, it is observed that none of the aforementioned applications provide such a feature. In this paper, a framework is developed with Google Maps APIs to create such a feature. This problem is mapped to the Traveling salesman problem and tried to solve it using algorithms known for approximating TSP such as Artificial Bee Colony Algorithm, Particle Swarm Optimization and Two-opt Algorithm. The framework is tested with these algorithms and found that, Particle Swarm Optimization gives the best possible route. © 2019 IEEE.Item Simulation of Indoor Localization and Navigation of Turtlebot 3 using Real Time Object Detection(Institute of Electrical and Electronics Engineers Inc., 2021) Nandkumar, C.; Shukla, P.; Varma, V.This paper proposes a method for indoor localization and navigation of Turtlebot 3 using Real Time Object Detection (RTOD). The robot is capable of recognizing the room it is placed inside based on the knowledge of positions of certain fixed arbitrary objects. The robot then proceeds to understand its position inside the room and is capable of moving to other locations. The robot is simulated using the ROS and Gazebo framework. The RTOD is trained to identify certain distinct objects like a rover, bowl, quadcopter and wheel based on which the robot is able to ascertain its location. © 2021 IEEE.Item Opportunities and Challenges in Development of Support System for Visually Impaired: A Survey(Institute of Electrical and Electronics Engineers Inc., 2023) Vijetha, U.; Geetha, V.Over the past few years, the usage of assistive technology by the visually impaired (VI) has significantly increased worldwide. These devices assist the VI in carrying out daily tasks efficiently, boosts their independence, thereby enhancing the quality of life. However, most of these technologies are very expensive and are not affordable by persons living in mid and low-economy countries. Advances in computer vision and deep learning have opened doors to the development of low-cost solutions for the visually impaired. This paper investigates the potential of a smartphone app to qualify as an affordable yet effective VI assistive device. We highlight the recent developments in computer vision and deep learning techniques that have the potential to provide innovative solutions for the benefit of VI community. We outline the strengths and weaknesses of different techniques and report on the unresolved issues and potential future directions in the context of a support system for the visually impaired. © 2023 IEEE.Item A study and implementation of mapping and speech recognition techniques for an autonomous mobile robot based on ROS(Inderscience Enterprises Ltd., 2017) Srinivasa Rao, H.; Desai, V.; Bhat, R.; Jayaprakash, S.; Sampangi, Y.Autonomous mobile robots work in close interaction with humans in environments such as homes, hospitals, public places and disaster areas. In autonomous mobile robots, the main constraints are safety, autonomy and efficiency in helping the humans. Given these constraints, developing the autonomous mobile robot technologies is a major challenge for both the industry and the research society. This paper work is about how an indoor autonomous mobile robot can work based on robot operating system and using Lidar and other sensors to create a map of an environment, and perform autonomous navigation with using capabilities like dynamic obstacle avoidance, speech recognition and video streaming. To achieve the above features, different algorithms like SLAM, AMCL, dynamic window approach algorithms, and CMU PocketSphinx speech recogniser are used. For video steaming, ROS web video server is used and the recorded video can be sent to a remote desktop system using ROS network. © 2017 Inderscience Enterprises Ltd.Item Navigation in GPS spoofed environment using m-best positioning algorithm and data association(Institute of Electrical and Electronics Engineers Inc., 2021) Pardhasaradhi, B.; Srihari, P.; Aparna., P.Intentionally misguiding a global positioning system (GPS) receiver has become a potential threat to almost all civilian GPS receivers in recent years. GPS spoofing is among the types of intentional interference, in which a spoofing device transmits spoofed signals towards the GPS receiver to alter the GPS positioning information. This paper presents a robust positioning algorithm, followed by a track filter, to mitigate the effects of spoofing. It is proposed to accept the authentic GPS signals and spoofed GPS signals into the positioning algorithm and perform the robust positioning with all possible combinations of authentic and spoofed pseudorange measurements. The pseudorange positioning algorithm is accomplished using an iterative least squares (ILS). Further, to efficiently represent the robust algorithm, the M-best position algorithm is proposed, in which a likelihood-based cost function optimizes the positions and only provides M-best positions at a given epoch. However, during robust positioning, the positions evolved due to spoofed pseudorange measurements are removed to overcome GPS spoofing. In order to remove the fake positions being evolved owing to wrong measurement associations in the ILS, a gating technique is applied within the Kalman filter (KF) framework. The navigation filter is a three-dimensional KF with a constant velocity (CV) model, all the position estimates evolved at a specific epoch are observations. Besides, to enhance this technique's performance, the track to position association is performed by using two data association algorithms: nearest neighbor (NN) and probabilistic data association (PDA). Simulations are carried out for GPS receiver positioning by injecting different combinations of spoofed signals into the receiver. The proposed algorithm's efficiency is given by a success rate metric (defined as the navigation track to follow the true trajectory rather than spoofing trajectory) and position root mean square error (PRMSE). © 2013 IEEE.Item 2D-VPC: An Efficient Coverage Algorithm for Multiple Autonomous Vehicles(Institute of Control, Robotics and Systems, 2021) Nair, V.G.; Guruprasad, K.R.In this paper, we address a problem of multi-robotic coverage, where an area of interest is covered by multiple sensors, each mounted on an autonomous vehicle such as an aerial or a ground mobile robot. The area of interest is first decomposed into grids of equal size and then partitioned into Voronoi cells. Each robot/sensor is assigned the task of covering the corresponding Voronoi cell. We propose an optimal gridding size and partitioning methodology that eliminate the coverage inefficiencies induced by the partitioning process. We carried out experiments using multiple quadcopters and mobile robots to demonstrate and validate the proposed multi-sensor coverage strategy. © 2021, ICROS, KIEE and Springer.Item GNSS Spoofing Detection and Mitigation in Multireceiver Configuration via Tracklets and Spoofer Localization(Institute of Electrical and Electronics Engineers Inc., 2022) Pardhasaradhi, B.; Gunnery, G.; Vandana, G.S.; Srihari, P.; Aparna., P.Global navigation satellite systems (GNSS) sensors estimate its position, velocity, and time (PVT) using pseudorange measurements. When there is no interference, the pseudoranges are due to authentic satellites, and the bearings is distinguishable. Whereas, in the presence of any intentional interference source like spoofer, the pseudorange measurements owing to spurious signals and all the bearings from the same direction. These spurious attacks yield either no position or falsified position to the GNSS receiver. This paper proposes to install multiple GNSS receivers on a vehicle (assumed to be cooperative) to detect and mitigate the spoofing attack. While installing multiple GNSS receivers, we assume that each GNSS receiver's relative position vector (RPV) is assumed to be known to other GNSS receivers. The installed GNSS receivers use the extended Kalman filter (EKF) framework to estimate their PVT. We proposed to calculate the equivalent-measurement and equivalent-measurement covariance of each GNSS receiver in the Cartesian coordinates in the tracklet framework. These tracklets are translated to the vehicle center using RPV to obtain translated-Tracklets. The translated tracklet based generalized likelihood ratio test (GLRT) is derived to detect the spoofing attack at a given epoch. In addition to that, these translated-Tracklets are processed in a batch least square (LS) framework to obtain the vehicle position. Once the attack is detected at a specific epoch, it quantifies that the position information is false. Moreover, another spoofing test is also formulated using DOA of signals. Once both the tests confirm the spoofing attack, the spoofer localization is performed using pseudo-updated states of GNSS receivers and acquired bearings in the iterative least-squares (ILS) framework. Mitigation of spoofing attack can be achieved either by projecting a null beam in the direction of the spoofer or by launching a counter-Attack on the spoofer. The simulation results demonstrate that the proposed algorithm detects spoofing attacks and ensures continuity in the navigation track. As the number of satellite signals increases, the algorithms provide better position root mean square error (PRMSE) for GNSS receivers track, vehicle track, and spoofer localization. © 2013 IEEE.Item Optimizing Reinforcement Learning-Based Visual Navigation for Resource-Constrained Devices(Institute of Electrical and Electronics Engineers Inc., 2023) Vijetha, U.; Geetha, V.Existing work on Deep reinforcement learning-based visual navigation mainly focuses on autonomous agents with ample power and compute resources. However, Reinforcement learning for visual navigation on resource-constrained devices remains an under-explored area of research, primarily due to challenges posed by processing high-dimensional visual inputs and making prompt decisions in realtime scenarios. To address these hurdles, we propose a State Abstraction Technique (SAT) designed to transform high-dimensional visual inputs into a compact representation, enabling simpler reinforcement learning agents to process the information and learn effective navigation policies. The abstract representation generated by SAT effortlessly serves as a versatile intermediary that bridges the gap between simulation and reality, enhancing the transferability of learned policies across various scenarios. Additionally, our reward shaping strategy uses the data provided by SAT to maintain a safe distance from obstacles, further improving the performance of navigation policies on resource-constrained devices. Our work opens up opportunities for navigation assistance and other applications in a variety of resource-constrained domains, where computational efficiency is crucial for practical deployment, such as guiding miniature agents on embedded devices or aiding visually impaired individuals through smartphone-integrated solutions. Evaluation of proposed approach on the AI2-Thor simulated environment demonstrates significant performance improvements over traditional state representations. The proposed method provides 84.18% fewer collisions, 28.96% fewer movement instructions and 11.3% higher rewards compared to the best alternative options available. Furthermore, we carefully account for real-world challenges by considering noise and motion blur during training, ensuring optimal performance during deployment on resource-constrained devices. © 2013 IEEE.Item Obs-tackle: an obstacle detection system to assist navigation of visually impaired using smartphones(Springer Science and Business Media Deutschland GmbH, 2024) Vijetha, U.; Geetha, V.As the prevalence of vision impairment continues to rise worldwide, there is an increasing need for affordable and accessible solutions that improve the daily experiences of individuals with vision impairment. The Visually Impaired (VI) are often prone to falls and injuries due to their inability to recognize dangers on the path while navigating. It is therefore crucial that they are aware of potential hazards in both known and unknown environments. Obstacle detection plays a key role in navigation assistance solutions for VI users. There has been a surge in experimentation on obstacle detection since the introduction of autonomous navigation in automobiles, robots, and drones. Previously, auditory, laser, and depth sensors dominated obstacle detection; however, advances in computer vision and deep learning have enabled it using simpler tools like smartphone cameras. While previous approaches to obstacle detection using estimated depth data have been effective, they suffer from limitations such as compromised accuracy when adapted for edge devices and the incapability to identify objects in the scene. To address these limitations, we propose an indoor and outdoor obstacle detection and identification technique that combines semantic segmentation with depth estimation data. We hypothesize that this combination of techniques will enhance obstacle detection and identification compared to using depth data alone. To evaluate the effectiveness of our proposed Obstacle detection method, we validated it against ground truth Obstacle data derived from the DIODE and NYU Depth v2 dataset. Our experimental results demonstrate that the proposed method achieves near 85% accuracy in detecting nearby obstacles with lower false positive and false negative rates. The demonstration of the proposed system deployed as an Android app-‘Obs-tackle’ is available at https://youtu.be/PSn-FEc5EQg?si=qPGB13tkYkD1kSOf . © 2024, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.