3D Camera and Lidar Utilization for Mobile Robot Navigation

Stefanczyk, Michal; Banachowicz, Konrad; Walęcki, M.; Winiarski, Tomasz

doi:10.14313/jamris_4-2013/28

Cited by 7 publications

(3 citation statements)

References 15 publications

(13 reference statements)

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“…It contains information relative to the LiDAR. The information we use from this message is the LaserScan.ranges which is an array of floats containing the value of the distance between the robot and its environment (360°) [17]. All orientations where distance is measured are separated by 1 degree.…”

Section: A Short Overviewmentioning

confidence: 99%

ROS-based architecture for robotic automatic mapping and intelligent obstacle avoidance using lidar and cameras

zhao¹,

liu²

2022

International Conference on Intelligent and Human-Computer Interaction Technology (IHCIT 2022)

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With the rapid development of robotics, intelligent obstacle avoidance robots are receiving more and more attention. They are heavily used in all aspects of production and development. The framework we chose to develop this time is based on an open-source platform called Robot Operating System (ROS), which acts as a digital twin in a modular configuration. There are now a large number of robots that choose to complete their architecture under the Robot Operating System (ROS), such as disaster relief robots, somatosensory robots, delivery robots, and more. In this article, we will introduce the ROS architecture of our project. This architecture enables robots to map and navigate autonomously in different environments with different sensors. This article will analyze the architecture we have made and explain the choice to make such an architecture. At the same time, we will also analyze our performance and point out which parts can be improved.

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Section: A Short Overviewmentioning

confidence: 99%

ROS-based architecture for robotic automatic mapping and intelligent obstacle avoidance using lidar and cameras

zhao¹,

liu²

2022

International Conference on Intelligent and Human-Computer Interaction Technology (IHCIT 2022)

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“…For seamless navigation, an informationdense representation of the dynamic scene, e.g., with explicitly tracked pedestrians, is usually beneficial [1]- [3]. But when transitioning away from test and training simulations to the real robot, complex fusion from multiple sensors and hardware-heavy post-processing steps are required to achieve such information-dense dynamics representations [4]- [8]. Here, also feature-rich but costly 3D lidar sensors are appealing [9], [10].…”

Section: Introductionmentioning

confidence: 99%

Learning Personalized Human-Aware Robot Navigation Using Virtual Reality Demonstrations from a User Study

Jorge

Corral

Bruckschen

et al. 2022

2022 31st IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)

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Foresighted robot navigation in dynamic indoor environments with cost-efficient hardware necessitates the use of a lightweight yet dependable controller. So inferring the scene dynamics from sensor readings without explicit object tracking is a pivotal aspect of foresighted navigation among pedestrians. In this paper, we introduce a spatiotemporal attention pipeline for enhanced navigation based on 2D lidar sensor readings. This pipeline is complemented by a novel lidar-state representation that emphasizes dynamic obstacles over static ones. Subsequently, the attention mechanism enables selective scene perception across both space and time, resulting in improved overall navigation performance within dynamic scenarios. We thoroughly evaluated the approach in different scenarios and simulators, finding good generalization to unseen environments. The results demonstrate outstanding performance compared to state-of-the-art methods, thereby enabling the seamless deployment of the learned controller on a real robot.

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“…Object tracking in buildings is needed for public safety and commercial interests [5]. Several studies have been conducted to avoid collisions, especially for dynamic obstacles [6], which is very useful for unknown robot environments [7,8]. For the security aspect of human-robot interaction, it is necessary to have an accurate and fast obstacle avoidance method as a basic requirement for intelligent robots.…”

Section: Introductionmentioning

confidence: 99%

2D mapping using omni-directional mobile robot equipped with LiDAR

2020

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A room map in a robot environment is needed because it can facilitate localization, automatic navigation, and also object searching. In addition, when a room is difficult to reach, maps can provide information that is helpful to humans. In this study, an omni-directional mobile robot equipped with a LiDAR sensor has been developed for 2D mapping a room. The YDLiDAR X4 sensor is used as an indoor scanner. Raspberry Pi 3 B single board computer (SBC) is used to access LiDAR data and then send it to a computer wirelessly for processing into a map. This computer and SBC are integrated in robot operating system (ROS). The movement of the robot can use manual control or automatic navigation to explore the room. The Hector SLAM algorithm determines the position of the robot based on scan matching of the LiDAR data. The LiDAR data will be used to determine the obstacles encountered by the robot. These obstacles will be represented in occupancy grid mapping. The experimental results show that the robot is able to follow the wall using PID control. The robot can move automatically to construct maps of the actual room with an error rate of 4.59%.

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3D Camera and Lidar Utilization for Mobile Robot Navigation

Cited by 7 publications

References 15 publications

ROS-based architecture for robotic automatic mapping and intelligent obstacle avoidance using lidar and cameras

ROS-based architecture for robotic automatic mapping and intelligent obstacle avoidance using lidar and cameras

Learning Personalized Human-Aware Robot Navigation Using Virtual Reality Demonstrations from a User Study

2D mapping using omni-directional mobile robot equipped with LiDAR

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