Improvement of the 2D SLAM system using Kinect sensor for indoor mapping

Ghani, Muhammad Fahmi Abdul; Sahari, Khairul Salleh Mohamed; Loo, Chu Kiong

doi:10.1109/scis-isis.2014.7044753

Cited by 16 publications

(6 citation statements)

References 6 publications

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“…Firstly, it is initialized to a maximum range value. Then the new point cloud coordinate values are calculated using equations (4) and (5). After that, angle, step, and range are calculated using the new coordinate.…”

Section: Virtual Floor Projection Methodsmentioning

confidence: 99%

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Detecting negative obstacle using Kinect sensor

Ghani

Sahari

2017

International Journal of Advanced Robotic Systems

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A robot must have a good understanding of the environment for autonomous navigation. Mobile robot using fixed laser range scanner can only detect obstacle on a plane level. This may cause important obstacles not to be appropriately detected. This will cause the map generated to be inaccurate and collision may actually occur during autonomous navigation. Microsoft Kinect is known to provide a low-cost 3-D data which can be used for mobile robot navigation. Many researchers focused on obstacles above ground level, and not negative obstacles such as holes or stairs. This article proposes the usage of Kinect sensor to detect negative obstacles and converts it into laser scan data. Positive obstacle is defined as the obstacle above the floor surface and negative obstacle is defined as the obstacle below the floor surface. Projection method is used to convert positive obstacle data from Kinect sensor to laser scan data. For negative obstacles detection, farthest point method and virtual floor projection method are used. The laser scan data from positive and negative obstacles are then combined to get an improved laser scan data, which includes all obstacles that are important for a robot to see. The negative obstacle detection methods are tested in simulated indoor environment and also experimental in a real environment. The simulation and experimental results have demonstrated the effectiveness of our proposed method to detect and map negative obstacles.

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Section: Virtual Floor Projection Methodsmentioning

confidence: 99%

“…This article is a revised and expanded version of an article entitled "Improvement of the 2D SLAM system using Kinect sensor for indoor mapping" 5 presented at SCIS and ISIS 2014, Kitakyushu, Japan, 3-6 December, 2014.…”

Section: Author's Notementioning

confidence: 99%

Detecting negative obstacle using Kinect sensor

Ghani

Sahari

2017

International Journal of Advanced Robotic Systems

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“…For SLAM technology, various systems or platforms have been introduced, such as the Lidar system [5], stereo camera [6] and RGBD-camera [7]. Some technologies based on SLAM can contribute to the improvement of mapping accuracy, such as a Pseudo-GNSS/INS module integrated framework with probabilistic SLAM [8], a 2D SLAM system using low-cost Kinect Sensor [9], prediction-based SLAM (P-SLAM) [10], graph-based hierarchical SLAM framework [11], semi-direct visual-inertial SLAM framework [12], and a CPU-only pipeline for SLAM [13]. Similar to traditional data fusion technology [14], SLAM with data fusion technologies has also been developed accordingly, such as a fusion of the RGB image and Lidar point cloud [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

A Novel Loop Closure Detection Approach Using Simplified Structure for Low-Cost LiDAR

Shi

et al. 2020

Sensors

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Reducing the cumulative error is a crucial task in simultaneous localization and mapping (SLAM). Usually, Loop Closure Detection (LCD) is exploited to accomplish this work for SLAM and robot navigation. With a fast and accurate loop detection, it can significantly improve global localization stability and reduce mapping errors. However, the LCD task based on point cloud still has some problems, such as over-reliance on high-resolution sensors, and poor detection efficiency and accuracy. Therefore, in this paper, we propose a novel and fast global LCD method using a low-cost 16 beam Lidar based on “Simplified Structure”. Firstly, we extract the “Simplified Structure” from the indoor point cloud, classify them into two levels, and manage the “Simplified Structure” hierarchically according to its structure salience. The “Simplified Structure” has simple feature geometry and can be exploited to capture the indoor stable structures. Secondly, we analyze the point cloud registration suitability with a pre-match, and present a hierarchical matching strategy with multiple geometric constraints in Euclidean Space to match two scans. Finally, we construct a multi-state loop evaluation model for a multi-level structure to determine whether the two candidate scans are a loop. In fact, our method also provides a transformation for point cloud registration with “Simplified Structure” when a loop is detected successfully. Experiments are carried out on three types of indoor environment. A 16 beam Lidar is used to collect data. The experimental results demonstrate that our method can detect global loop closures efficiently and accurately. The average global LCD precision, accuracy and negative are approximately 0.90, 0.96, and 0.97, respectively.

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“…An EKF-SLAM was implemented using ROS framework in real environment is presented in [8]. An improvement of RBPF-SLAM using kinect camera on a developed system using ROS platform is shown in [9], where the experiment was done in gazebo simulated environment and real environment. In this paper, we show the implementation of EKF-SLAM on cloud based on ROS platform.…”

Section: Introductionmentioning

confidence: 99%

Implementation of Mobile Robot’s Navigation using SLAM based on Cloud Computing

Hasan¹,

Mohammed²

2017

ETJ

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This paper is concerned with the implementation of EKF-SLAM (Extended Kalman Filter-Simultaneous Localization and Mapping) algorithm using a cloud computing architecture based on ROS (Robot Operating System). The localization and mapping is essential step to navigate a mobile robot in unknown environment. The implemented EKF-SLAM has used a landmark that sensed using IR Emitter sensor provided by the Kinect camera to update a map of the environment and simultaneously estimate the robot's position and orientation within the map. The implementation was done using three parts. The first one was the TurleBot Mobile robot with the Kinect camera, which was simulated in Gazebo environment. The second part was the EKF-SLAM running under the MATLAB to generate the Map and Location data. The third part was the ROS Master node, which runs on the cloud to enable part one and two to communicate using topics. The scan data from Kinect camera and the location data from the odometer is transferred from the first part to the second part through ROS Master node after impaired with zero mean Gaussian noise . Then the second part performs EKF-SLAM and transmit the corrections to the first part through the ROS Master node as well.

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Improvement of the 2D SLAM system using Kinect sensor for indoor mapping

Cited by 16 publications

References 6 publications

Detecting negative obstacle using Kinect sensor

Detecting negative obstacle using Kinect sensor

A Novel Loop Closure Detection Approach Using Simplified Structure for Low-Cost LiDAR

Implementation of Mobile Robot’s Navigation using SLAM based on Cloud Computing

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