Monocular Localization Using Invariant Image Feature Matching to Assist Navigation

Upadhyay, Vikas; Balakrishnan, M.

doi:10.1007/978-3-031-08648-9_21

Cited by 3 publications

(3 citation statements)

References 23 publications

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“…Upadhyay et al [23] introduce a novel approach to indoor visual positioning utilizing a single camera. This method employs intelligent visual feature selection and real-time matching, leveraging a monocular camera to capture and transform video route information into sparse and invariant point-based  ISSN: 2252-8938 Int J Artif Intell, Vol.…”

Section: Related Workmentioning

confidence: 99%

Real-time indoor tracking for augmented reality using computer vision technique

Shewail,

H. Zayed,

A. M. Elsayed

2024

IJ-AI

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In recent times, there has been an increase in the stability and integration of augmented reality (AR) technology in everyday applications. AR relies on tracking techniques to capture the characteristics of the surrounding environment. Tracking falls into two categories: outdoor and indoor. While outdoor tracking predominantly relies on the global positioning system (GPS), it is performance indoors is hindered by imprecise GPS signals. Indoor tracking offers a solution for navigating complex indoor environments. This paper introduces an indoor tracking system that combines smartphone sensor data and computer vision using the oriented features from accelerated and segments test and rotated binary robust independent elementary features (ORB) algorithm for feature extraction, along with brute force match (BFM) and k-nearest neighbor (KNN) for matching. This approach outperforms previous systems, offering efficient navigation without relying on pre-existing maps. The system uses the A* algorithm to find the shortest path and cloud computing for data storage. Experimental results demonstrate an impressive 99% average accuracy within a 7-10 cm error range, even in scenarios with varying distances. Moreover, all users successfully reached their destinations during the experiments. This innovative model presents a promising advancement in indoor tracking, enhancing the accuracy and effectiveness of navigation in complex indoor spaces

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Section: Related Workmentioning

confidence: 99%

Real-time indoor tracking for augmented reality using computer vision technique

Shewail,

H. Zayed,

A. M. Elsayed

2024

IJ-AI

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“…The camera localization problem determines the pose in a known environment that matches a query image that fits the model with dataset images. Monocular cameras [11] and depth cameras that combine RGB and RGB-D images are used for the camera pose estimation problem. Each pose p includes the camera location and rotation.…”

Section: Problem Identificationmentioning

confidence: 99%

A Review of Recurrent Neural Network Based Camera Localization for Indoor Environments

et al. 2023

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Camera localization involves the estimation of the camera pose of an image from a random scene. We used a single image or sequence of images or videos as the input. The output depends on the representation of the scene and method used. Several computer vision applications, such as robot navigation and safety inspection, can benefit from camera localization. Camera localization is used to determine the position of an object on the camera in an image containing multiple images in a sequence. Structure-based localization techniques have achieved considerable success owing to a combination of image matching and coordinate regression. Absolute and relative pose regression techniques can provide end-to-end learning; however, they exhibit poor accuracies. Despite the rapid growth in computer vision, there has been no thorough review of the categorization, evaluation, and synthesis of structures and regression-based techniques. Input format and loss strategies for recurrent neural networks (RNN) have not been adequately described in the literature. The main topic is indoor camera pose regression, which is a part of the camera localization techniques. First, we discuss certain application areas for camera localization. We then discuss different camera localization techniques, such as feature and structure-based, absolute and relative pose regression techniques, and simultaneous localization and mapping (SLAM). We evaluated the frequently used datasets and qualitatively compared the absolute and relative camera pose estimation approaches. Finally, we discuss potential directions for future research, such as optimizing the computational cost of the features and evaluating the end-to-end characteristics of multiple cameras.INDEX TERMS Camera pose regression, absolute pose regression, indoor positioning, camera localization, robot navigation, SLAM.

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“…The global navigation satellite system (GNSS) [4] is used in navigation systems; it can reach the centimeter level in open areas with real-time kinematic (RTK) [5] technology. When the signal is blocked, high positioning accuracy can be attained by camera-based [6] or LiDARbased [7] feature-matching technology. More details about localization are not discussed here, but can be found in the literature.…”

Section: Introductionmentioning

confidence: 99%

Accelerated and Refined Lane-Level Route-Planning Method Based on a New Road Network Model for Autonomous Vehicle Navigation

Ding

et al. 2023

WEVJ

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Lane-level route planning is a critical issue for a lane-level navigation system for autonomous vehicles. Current route-planning methods mainly focus on the road level and applying them directly to search for lane-level routes results in a reduction in search efficiency. In addition, previously developed lane-level methods lack consideration for vehicle characteristics and adaptability to multiple road network structures. To solve this issue, this study proposes an accelerated and refined lane-level route-planning algorithm based on a new lane-level road network model. First, five sub-layers are designed to refine the internal structure of the divided road and intersection areas so that the model can express multiple variations in road network structures. Then, a multi-level route-planning algorithm is designed for sequential planning at the road level, lane group level, lane section level, and lane level to reduce the search space and significantly improve routing efficiency. Last, an optimal lane determination algorithm considering traffic rules, vehicle characteristics, and optimization objectives is developed at the lane level to find the optimal lanes on roads with different configurations, including those with a constant or variable number of lanes while satisfying traffic rules and vehicle characteristics. Tests were performed on simulated road networks and a real road network. The results demonstrate the algorithm’s better adaptability to changing road network structures and vehicle characteristics compared with past hierarchical route planning, and its higher efficiency compared with direct route planning, past hierarchical route planning, and the Apollo route-planning method, which can better support autonomous vehicle navigation.

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Monocular Localization Using Invariant Image Feature Matching to Assist Navigation

Cited by 3 publications

References 23 publications

Real-time indoor tracking for augmented reality using computer vision technique

Real-time indoor tracking for augmented reality using computer vision technique

A Review of Recurrent Neural Network Based Camera Localization for Indoor Environments

Accelerated and Refined Lane-Level Route-Planning Method Based on a New Road Network Model for Autonomous Vehicle Navigation

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