SLAM � Map Building and Navigation via ROS

Pajaziti, Arbnor

doi:10.18201/ijisae.08103

Cited by 22 publications

(18 citation statements)

References 4 publications

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“…This sensory information in terms of occupancy grid mapping was further used by the mobile robot along with different algorithms like Monte Carlo Localization, Adaptive Monte Carlo Localization (AMCL), various estimation filters, etc. for the navigation of mobile robot (Levinson and Thrun, 2010; Pajaziti, 2014; Zaman et al , 2011). Laser scanner is the most widely used range sensor because of features like fast response, accuracy, compactness, etc.…”

Section: Related Work and Contributionmentioning

confidence: 99%

“…For the autonomous navigation of mobile robot, mapping and localization are the vital parameters to be examined. `Mapping is done by using a laser scanner (range sensors), and the localization is attained from odometry information of the mobile robot (Pajaziti, 2014). The laser grid maps of the Environment #1 are generated by ROS in all three different ambient conditions as shown in Figures 11 and 12 with the implementation of the conventional approaches (Marni et al , 2016 ; N. Mansharamani, 1995; Pajaziti, 2014; Zaman et al , 2011).…”

Section: Experimental Setup To Study the Effect Of Ambient Light Conditionsmentioning

confidence: 99%

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Error analysis of laser scanner for robust autonomous navigation of mobile robot in diverse illumination environment

Singh

Nagla

2018

WJE

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Purpose Modern service robots are designed to work in a complex indoor environment, in which the robot has to interact with the objects in different ambient light intensities (day light, tube light, halogen light and dark ambiance). The variations in sudden ambient light intensities often cause an error in the sensory information of optical sensors like laser scanner, which reduce the reliability of the sensor in applications such as mapping, path planning and object detection of a mobile robot. Laser scanner is an optical sensor, so sensory information depends upon parameters like surface reflectivity, ambient light condition, texture of the targets, etc. The purposes of this research are to investigate and remove the effect of variation in ambient light conditions on the laser scanner to achieve robust autonomous mobile robot navigation. Design/methodology/approach The objective of this study is to analyze the effect of ambient light condition (dark ambiance, tube light and halogen bulb) on the accuracy of the laser scanner for the robust autonomous navigation of mobile robot in diverse illumination environments. A proposed AIFA (Adaptive Intensity Filter Algorithm) approach is designed in robot operating system (ROS) and implemented on a mobile robot fitted with laser scanner to reduce the effect of high-intensity ambiance illumination of the environment. Findings It has been experimentally found that the variation in the measured distance in dark is more consistent and accurate as compared to the sensory information taken in high-intensity tube light/halogen bulbs and in sunlight. The proposed AIFA approach is implement on a laser scanner fitted on a mobile robot which navigates in the high-intensity ambiance-illuminating complex environment. During autonomous navigation of mobile robot, while implementing the AIFA filter, the proportion of cession with the obstacles is reduce to 23 per cent lesser as compared to conventional approaches. Originality/value The proposed AIFA approach reduced the effect of the varying ambient light conditions in the sensory information of laser scanner for the applications such as autonomous navigation, path planning, mapping, etc. in diverse ambiance environment.

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Section: Related Work and Contributionmentioning

confidence: 99%

Section: Experimental Setup To Study the Effect Of Ambient Light Conditionsmentioning

confidence: 99%

Error analysis of laser scanner for robust autonomous navigation of mobile robot in diverse illumination environment

Singh

Nagla

2018

WJE

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“…If the laser range finder is placed horizontally like in many cases, obstacles which are lower or higher than the sensor level cannot be detected by the robot which may cause robot to collide with them. Pajaziti 8 has tested the 2-D SLAM system in simulated environment where all the obstacles are in sensor's height. Zug et al 9 have compared between laser range scanner and Kinect sensor for 2-D mapping, where they tested both in an environment where all obstacles are high enough for the laser scanner to detect.…”

Section: Related Workmentioning

confidence: 99%

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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“…(1) In simultaneous localization and mapping (SLAM), the robot starts from an unknown environment and acts in this environment, and continuously repeats the information of the environment to achieve its own positioning and status. Then, build an environmental map one by one based on the surrounding environment information constructed by its own location [ 14 , 15 , 16 , 17 , 18 , 19 ]. Lidar SLAM has high measurement accuracy but is expensive.…”

Section: Introductionmentioning

confidence: 99%

High-Efficiency Automatic Recharging Mechanism for Cleaning Robot Using Multi-Sensor

Chang

Tang

et al. 2018

Sensors

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Cleaning robot has the highest penetration rate among the service robots. This paper proposes a high-efficiency mechanism for an intelligent cleaning robot automatically returns to charging in a short time when the power is insufficient. The proposed mechanism initially combines the robot’s own motor encoder with neural network linear regression to calculate the moving distance and rotation angle for the location estimation of the robot itself. At the same time, a self-rotating camera is applied to scan the number of infrared spots on the docking station to find the location of the docking station so that the cleaning robot returns to charging properly in two stages, existing infrared range and extended infrared range. In addition, six-axis acceleration and ultrasound are both applied to deal with the angle error that is caused by collision. Experimental results show that the proposed recharging mechanism significantly improves the efficiency of recharging.

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SLAM � Map Building and Navigation via ROS

Cited by 22 publications

References 4 publications

Error analysis of laser scanner for robust autonomous navigation of mobile robot in diverse illumination environment

Error analysis of laser scanner for robust autonomous navigation of mobile robot in diverse illumination environment

Detecting negative obstacle using Kinect sensor

High-Efficiency Automatic Recharging Mechanism for Cleaning Robot Using Multi-Sensor

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