Indoor Map Generation from Multiple LIDAR Point Clouds

Yoshisada, Hikaru; Yamada, Yuma; Hiromori, Akihito; Yamaguchi, Hirozumi; Higashino, Teruo

doi:10.1109/smartcomp.2018.00076

Cited by 9 publications

(4 citation statements)

References 12 publications

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“…In our Hitonavi system, we introduced an algorithm designed to create an indoor map through the point clouds sourced from multiple LIDARs, which scan the identical space from different positions Yoshisada et al (2018). For the sake of simplification, we have assumed that the LIDARs perform scans in a horizontal manner, meaning the beams are emitted in horizontal directions, but this concept is extensible to 3D spaces.…”

Section: Point Cloud Integrationmentioning

confidence: 99%

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Towards Intelligent Environments: Human Sensing through Point Cloud

Yamaguchi,

Rizk,

Amano

et al. 2024

Preprint

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''Society 5.0'' seeks to enhance well-being by integrating the Internet of Things to connect people, objects, and environments, which aims to gather and utilize data from the physical world in cyberspace fostering new values and knowledge sharing. It extends the concept of Cyber-Physical Systems by focusing on human-centric sensing and AI-driven analysis to capture and understand the nuances of human behaviors and their environments. To realize the concept, intelligent environments, focusing on creating sensor-enhanced spaces capable of recognizing and interpreting human behaviors and needs, are essential. To explore how we realize the intelligent environment, this paper highlights the current technologies and applications of human sensing and tracking that enables such intelligent environments.Specifically, it introduces the development and application of LiDAR-based human sensing and tracking technologies, including the Hitonavi and Hitonavi-µ systems, designed for multi-person tracking in extensive areas and for enabling person detection and activity recognition on edge devices, respectively. Moreover, the services and applications of the introduced methodologies to smart buildings, shopping malls, and elderly care have been explained to showcase their potential to enhance safety, convenience, and understanding of human behavior within intelligent environment. Throughout this paper, we present our vision for creating a reliable intelligent environment, which is fundamentally supported by sophisticated human sensing technologies.

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Section: Point Cloud Integrationmentioning

confidence: 99%

“…Through the repeated iterations of this integration, the algorithm can refine and enhance the accuracy of the integration. For more technical details, the authors may refer to Yoshisada et al (2018).…”

Section: Point Cloud Integrationmentioning

confidence: 99%

Towards Intelligent Environments: Human Sensing through Point Cloud

Yamaguchi,

Rizk,

Amano

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

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“…Among the most common interfaces of such kind is RViz, embedded in the robot operating system (ROS) [3], being utilized in the most robotic devices. Such graphical interfaces commonly visualize the indoor layout, charted with the SLAM (simultaneous localization and mapping) algorithm [4], or by other means [5]. The layout is used for specifying and editing of mobile robot target points, but operator has no information, whether certain point is accessible for the robot.…”

Section: Introductionmentioning

confidence: 99%

Algorithm of Target Point Assignment for Robot Path Planning Based on Costmap Data

Kuznetsov

Kozyr

Levonevskiy

2021

Smart Innovation, Systems and Technologies

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Most mobile robots which use the operating systems (ROS) employ costmap-based path planner for navigation in environment. Besides the systems, in which robotic devices function in completely autonomous manner, also such systems exist, where user specifies in a self-contained interface the target point for robot motion, without using any costmap-related data. In such settings, the robot sometimes cannot reach the user-specified point because it is treated as an obstacle on the costmap. To solve this problem an algorithm was developed, which accepts the coordinates of the target point, specified through user interface, and finds possible target points in vicinity of the specified one, regarding the predefined distance constraints. Conceptually, the search of possible points consists in looping through costmap cells of the global cost planner in orthogonally related directions from the obtained point and in defining of the preferable target points, upon reaching which the task is considered to be completed. In this paper, the algorithm was tested as part of the navigational module of the robot. Experiments were performed, particularly, in the Gazebo simulation environment; robot model TURTLEBOT3 Waffle Pi was also utilized. The experimental results showed that this module can be combined with any planner. When the value of validation step was set at the minimum value of 0.01 m, it was required no more than 55 ms to find a target point. The minimum duration of such search was 1 ms.

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“…Mobile mapping is a highly efficient method for constructing an indoor map. Simultaneous Localization and Mapping (SLAM) is a popular and applicable method for mobile mapping in a GNSS-denied area, especially indoor environments [1,2].…”

Section: Introductionmentioning

confidence: 99%

An Orthogonal Weighted Occupancy Likelihood Map with IMU-Aided Laser Scan Matching for 2D Indoor Mapping

Qian

Zhang

Tang

et al. 2019

Sensors

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An indoor map is a piece of infrastructure associated with location-based services. Simultaneous Localization and Mapping (SLAM)-based mobile mapping is an efficient method to construct an indoor map. This paper proposes an SLAM algorithm based on a laser scanner and an Inertial Measurement Unit (IMU) for 2D indoor mapping. A grid-based occupancy likelihood map is chosen as the map representation method and is built from all previous scans. Scan-to-map matching is utilized to find the optimal rigid-body transformation in order to avoid the accumulation of matching errors. Map generation and update are probabilistically motivated. According to the assumption that the orthogonal is the main feature of indoor environments, we propose a lightweight segment extraction method, based on the orthogonal blurred segments (OBS) method. Instead of calculating the parameters of segments, we give the scan points contained in blurred segments a greater weight during the construction of the grid-based occupancy likelihood map, which we call the orthogonal feature weighted occupancy likelihood map (OWOLM). The OWOLM enhances the occupancy likelihood map by fusing the orthogonal features. It can filter out noise scan points, produced by objects, such as glass cabinets and bookcases. Experiments were carried out in a library, which is a representative indoor environment, consisting of orthogonal features. The experimental result proves that, compared with the general occupancy likelihood map, the OWOLM can effectively reduce accumulated errors and construct a clearer indoor map.

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Indoor Map Generation from Multiple LIDAR Point Clouds

Cited by 9 publications

References 12 publications

Towards Intelligent Environments: Human Sensing through Point Cloud

Towards Intelligent Environments: Human Sensing through Point Cloud

Algorithm of Target Point Assignment for Robot Path Planning Based on Costmap Data

An Orthogonal Weighted Occupancy Likelihood Map with IMU-Aided Laser Scan Matching for 2D Indoor Mapping

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