Localization and mapping of surveillance cameras in city map

This chapter focuses on a few issues related to video evidence and law, especially with respect to American law. The first issue is the history of the use of video evidence in court. The second issue involves constitutional protections regarding the state’s use of surveillance cameras. The chapter then turns to the Supreme Court case Scott v. Harris to raise concerns about the use of video evidence as not just proof but “truth.” These are of course just a sampling of the issues that the topic of video evidence could raise. The hope is that this chapter will spur further inquiry on the part of the reader.

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Autonomous Mobile Robot Navigation in Sparse LiDAR Feature Environments

Applied Sciences ◽

10.3390/app11135963 ◽

2021 ◽

Vol 11 (13) ◽

pp. 5963

Author(s):

Phuc Thanh-Thien Nguyen ◽

Shao-Wei Yan ◽

Jia-Fu Liao ◽

Chung-Hsien Kuo

Keyword(s):

Robot Navigation ◽

Tracking Error ◽

Unmanned Vehicles ◽

Mobile Robot Navigation ◽

Selection Strategy ◽

Localization And Mapping ◽

Pure Pursuit ◽

Stable Performance ◽

Large Curve ◽

Moving Speed

In the industrial environment, Autonomous Guided Vehicles (AGVs) generally run on a planned route. Among trajectory-tracking algorithms for unmanned vehicles, the Pure Pursuit (PP) algorithm is prevalent in many real-world applications because of its simple and easy implementation. However, it is challenging to decelerate the AGV’s moving speed when turning on a large curve path. Moreover, this paper addresses the kidnapped-robot problem occurring in spare LiDAR environments. This paper proposes an improved Pure Pursuit algorithm so that the AGV can predict the trajectory and decelerate for turning, thus increasing the accuracy of the path tracking. To solve the kidnapped-robot problem, we use a learning-based classifier to detect the repetitive pattern scenario (e.g., long corridor) regarding 2D LiDAR features for switching the localization system between Simultaneous Localization And Mapping (SLAM) method and Odometer method. As experimental results in practice, the improved Pure Pursuit algorithm can reduce the tracking error while performing more efficiently. Moreover, the learning-based localization selection strategy helps the robot navigation task achieve stable performance, with 36.25% in completion rate more than only using SLAM. The results demonstrate that the proposed method is feasible and reliable in actual conditions.

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Comparison of different SLAM approaches for a driverless race car

tm - Technisches Messen ◽

10.1515/teme-2021-0004 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Nick Le Large ◽

Frank Bieder ◽

Martin Lauer

Keyword(s):

Kalman Filter ◽

Extended Kalman Filter ◽

Ground Truth ◽

Time Constraints ◽

Differential Gps ◽

Real World Data ◽

Race Car ◽

Localization And Mapping ◽

Slam Algorithm ◽

Computational Resources

Abstract For the application of an automated, driverless race car, we aim to assure high map and localization quality for successful driving on previously unknown, narrow race tracks. To achieve this goal, it is essential to choose an algorithm that fulfills the requirements in terms of accuracy, computational resources and run time. We propose both a filter-based and a smoothing-based Simultaneous Localization and Mapping (SLAM) algorithm and evaluate them using real-world data collected by a Formula Student Driverless race car. The accuracy is measured by comparing the SLAM-generated map to a ground truth map which was acquired using high-precision Differential GPS (DGPS) measurements. The results of the evaluation show that both algorithms meet required time constraints thanks to a parallelized architecture, with GraphSLAM draining the computational resources much faster than Extended Kalman Filter (EKF) SLAM. However, the analysis of the maps generated by the algorithms shows that GraphSLAM outperforms EKF SLAM in terms of accuracy.

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Early-Anomaly Prediction in Surveillance Cameras for Security Applications

2021 International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC) ◽

10.1109/miucc52538.2021.9447668 ◽

2021 ◽

Author(s):

Mario Emad ◽

Michael Ishack ◽

Mohamed Ahmed ◽

Mohamed Osama ◽

Mohamed Salah ◽

...

Keyword(s):

Security Applications ◽

Surveillance Cameras

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A survey: which features are required for dynamic visual simultaneous localization and mapping?

Visual Computing for Industry Biomedicine and Art ◽

10.1186/s42492-021-00086-w ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Zewen Xu ◽

Zheng Rong ◽

Yihong Wu

Keyword(s):

Simultaneous Localization And Mapping ◽

Dynamic Environments ◽

Visual Features ◽

Advantages And Disadvantages ◽

Intelligent Robots ◽

Localization And Mapping ◽

High Level ◽

Static World ◽

Robotic Applications ◽

Significant Attention

AbstractIn recent years, simultaneous localization and mapping in dynamic environments (dynamic SLAM) has attracted significant attention from both academia and industry. Some pioneering work on this technique has expanded the potential of robotic applications. Compared to standard SLAM under the static world assumption, dynamic SLAM divides features into static and dynamic categories and leverages each type of feature properly. Therefore, dynamic SLAM can provide more robust localization for intelligent robots that operate in complex dynamic environments. Additionally, to meet the demands of some high-level tasks, dynamic SLAM can be integrated with multiple object tracking. This article presents a survey on dynamic SLAM from the perspective of feature choices. A discussion of the advantages and disadvantages of different visual features is provided in this article.

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