In this contribution we introduce a framework for precise vehicle localization in dense urban environments which are characterized by high rates of dynamic and semi-static objects. The proposed localization method is ...
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In this contribution we introduce a framework for precise vehicle localization in dense urban environments which are characterized by high rates of dynamic and semi-static objects. The proposed localization method is specifically designed to handle inconsistencies between map material and sensor measurements. This is achieved by means of a robust map matching procedure based on the Fourier-Mellin transformation (FMT) for global vehicle pose estimation. Accurate and reliable relative localization is obtained from a LiDAR odometry. Consistency checks based on the cumulative sum (CUSUM) test are instrumented for rejection of inconsistent map matching results from the fusion procedure. Our key contributions are: i) Introduction and adaptation of a spectral map matching procedure based on the FMT for urban automated driving, ii) Presentation of a framework for efficient and robust localization in dense urban environments based on a novel LiDAR odometry, map matching, wheel odometry and GPS, iii) Proposal of a procedure for localization integrity monitoring which leads to significantly increased pose estimation accuracy. Evaluation results show the superior performance of the proposed approach compared to another state-of-the-art localization algorithm for a challenging urban dataset. All maps were recorded two years in advance of the evaluation test run. Furthermore, different LiDAR-based sensor setups were used for mapping and localization.
In this article, we propose a novel navigation framework that leverages a two layered graph representation of the environment for efficient large-scale exploration, while it integrates a novel uncertainty awareness sc...
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ISBN:
(数字)9798350384574
ISBN:
(纸本)9798350384581
In this article, we propose a novel navigation framework that leverages a two layered graph representation of the environment for efficient large-scale exploration, while it integrates a novel uncertainty awareness scheme to handle dynamic scene changes in previously explored areas. The framework is structured around a novel goal oriented graph representation, that consists of, i) the local sub-graph and ii) the global graph layer respectively. The local sub-graphs encode local volumetric gain locations as frontiers, based on the direct pointcloud visibility, allowing fast graph building and path planning. Additionally, the global graph is build in an efficient way, using node-edge information exchange only on overlapping regions of sequential sub-graphs. Different from the state-of-the-art graph based exploration methods, the proposed approach efficiently re-uses sub-graphs built in previous iterations to construct the global navigation layer. Another merit of the proposed scheme is the ability to handle scene changes (e.g. blocked pathways), adaptively updating the obstructed part of the global graph from traversable to not-traversable. This operation involved oriented sample space of a path segment in the global graph layer, while removing the respective edges from connected nodes of the global graph in cases of obstructions. As such, the exploration behavior is directing the robot to follow another route in the global re-positioning phase through path-way updates in the global graph. Finally, we showcase the performance of the method both in simulation runs as well as deployed in real-world scene involving a legged robot carrying camera and lidar sensor.
In the era of modern world, lot of human lives are engaged with this perilous procedure of observation. Reconnaissance is a perilous activity. There is consistently a danger of getting trapped in spying, so we plan to...
In the era of modern world, lot of human lives are engaged with this perilous procedure of observation. Reconnaissance is a perilous activity. There is consistently a danger of getting trapped in spying, so we plan to discover safety with no risk. Doppler radar used for producing true results considering all army activity involved with data during transmission. This project presents an approach in detecting an intruder in a border using Doppler radar-based Intrusion Detection Systems which can be mounted on watch towers, grill fences, top of trees etc., Experimental results show that the use of the display as classifiers are more efficient in malicious intruder accurately. The data collected from the sensor and transmit through the Lora WAN to the hub. The performance of the Intrusion Detection System (IDS) is evaluated in comparison to other classifiers, displaying the testing accuracy, sensitivity, specificity, and precision. Doppler based Intrusion Detection Systems have shown increased efficiency and low false positives. It has the advantages of working in all weather conditions such as heavy sunshine, heavy rainfall with proper data.
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