Map building of indoor environment using laser range finder and geometrics

Kim, Eun Kyeong; Cho, Hyunhak; Jang, Eunseok; Park, Myung Kuk; Kim, Sungshin

doi:10.1109/aim.2015.7222711

Cited by 5 publications

(6 citation statements)

References 10 publications

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“…Figure 14 shows the comparison's graph of calculated and real distance of detected object which is obtained from Table 3 and Table 4. In [2] the average error of feature points is 5.40 cm, whereas in this study was 3.82 cm. This result indicates that there has been a decrease in the measurement gap of 29.26% from the previous study.…”

Section: Experimental Results and Analysiscontrasting

confidence: 56%

“…In this case, a map of the environment may be used by the mobile robot. Using the map, the robot can localize itself [2], identify the position of the obstacle, and all at once to avoid collisions against these obstacles [3]. Moreover, the map will be useful to give a safe and efficient information of the environment in order to reach a certain place or position.…”

Section: Introductionmentioning

confidence: 99%

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Grid-Edge-Depth Map Building Employing Sad With Sobel Edge Detector

Rahmani

Harjoko

Priyambodo

2017

International Journal on Smart Sensing and Intelligent Systems

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-This paper presents a new method of a map building which is suitable for a wheeled robot. The 2D map represents the obstacle's position and distance in the environment. The information of the obstacles obtained from a calibrated stereo camera. The stereo images size were 320x240 pixels. Hereafter the images were rectified and the disparity map was built using a Sum of Absolute Difference (SAD) algorithm. The depth map was calculated using disparity map, focal length, and baseline parameter values. In order to detect the obstacles, Sobel edge detection was implemented. The edge detection image was compared and substituted with the depth map which is resulting edge-depth map. The edge-depth map was divided into 25 grids (5 grids horizontal and 5 grids vertical). Finally, the minimum depth of detected obstacles for each grid was calculated. This process was resulting in a grid-edge-depth map (GED map). The proposal has been tested with a mobile robot in 5x3 meters living environment. Finally, experimental results are presented. The average error of feature points in the previous study was 5.40 cm, whereas in this study is 3.82 cm. There has been a decrease in the measurement gap of 29.26% from the previous study.

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Section: Experimental Results and Analysiscontrasting

confidence: 56%

Section: Introductionmentioning

confidence: 99%

Grid-Edge-Depth Map Building Employing Sad With Sobel Edge Detector

Rahmani

Harjoko

Priyambodo

2017

International Journal on Smart Sensing and Intelligent Systems

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show abstract

“…Then the camera sensor also has good accuracy, variety of uses, and relatively unlimited range. Robots equipped with sensors can then be programmed to have capabilities such as mapping, random exploration, and autonomous navigation [12] [13]. Autonomous navigation divided into three categories: map-based, map-building, and mapless.…”

Section: Fig 1 Grid-edge-depth Mapmentioning

confidence: 99%

“…Map-based navigation systems tend for the environment to remain unchanged for quite an extended period. Therefore in the navigation process, the robot can utilize a static map that contains in detail the position of the obstacle and also information on its distance from a particular position, for example from the initial position the robot move [12][14] [15]. Another autonomous robot navigation category is map-building.…”

Section: Fig 1 Grid-edge-depth Mapmentioning

confidence: 99%

Early Model of Vision-Based Obstacle Mapping Utilizing Grid-Edge-Depth Map

Rahmani,

Harjoko,

Priyambodo

et al. 2019

IJEAT

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This paper described a new method of obstacle mapping in an indoor environment utilizing a Grid-edge-depth map. The Grid-edge-depth map contained the information of distance and relative position of the object in the front of the robot. This mapping method utilized this information to mark off the visible obstacle/s in a particular virtual map. The 2D map created as a representative of the environment using a 300 by 500 pixels image. Every pixel represents a one by one cm of the environment and the obstacle's size. The obstacle's size was 30 by 30 pixels when it mapped by the system. It was a fixed size in the mapping process since the system cannot calculate the dimension of the detected obstacle. If the obstacle detected, the system checked its distance in GED-map. Then the system calculated the obstacle’s position against the goal, and finally map it in the 2D map. In this case, the proposed method in building a 2D map of the obstacle in the indoor environment combined with the rules to decide the direction of the mobile robot. The rules used to avoid the collision to the obstacle. The evaluation of the method showed that the system could map the detected obstacles, the initial position, and the goal’s relatif distance and position. The robot also reaches the goal position while avoiding the collision to the obstacle.

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“…38 Various area search and map building problems for mobile robots have attracted a lot of attention in the robotics community; see, e.g., refs. [3,15,17,21,25,27,28,31,37,39,41]. Recent publications in this field present many achievements in both single robot mapping and multi-robot mapping.…”

Section: Introductionmentioning

confidence: 99%

A safe area search and map building algorithm for a wheeled mobile robot in complex unknown cluttered environments

Savkin

2017

Robotica

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SUMMARYIn this paper, a safe map building and area search algorithm for a mobile robot in a closed unknown environment with obstacles is presented. A range finder sensor is used to detect the environment. The objective is to perform a complete search of the environment and build a complete map of it while avoiding collision with the obstacles. The developed robot navigation algorithm is randomized. It is proved that with probability 1 the robot completes its task in a finite time. Computer simulations and experiments with a real Pioneer-3DX robot confirm the performance of the proposed method.

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Map building of indoor environment using laser range finder and geometrics

Cited by 5 publications

References 10 publications

Grid-Edge-Depth Map Building Employing Sad With Sobel Edge Detector

Grid-Edge-Depth Map Building Employing Sad With Sobel Edge Detector

Early Model of Vision-Based Obstacle Mapping Utilizing Grid-Edge-Depth Map

A safe area search and map building algorithm for a wheeled mobile robot in complex unknown cluttered environments

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