.png){kind=icon}
返回
Enhanced maximum likelihood grid map with reprocessing incorrect sonar measurements
.png){kind=icon}
PRE
AI
DOI：10.1007/s10514-013-9340-5.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper, we address the problem of building a grid map as accurately as possible using inexpensive and error-prone sonar sensors. In this research area, incorrect sonar measurements, which fail to detect the nearest obstacle in their beamwidth, generally have been dealt with in the same manner as correct measurements or have been excluded from the mapping. In the former case, the map quality may be severely degraded. In the latter case, the resulting map may have insufficient information after the incorrect measurements are removed because only correct measurements are frequently insufficient to cover the whole environment. We propose an efficient grid-mapping approach that incorporates incorrect measurements in a specialized manner to build a better map; we call this the enhanced maximum likelihood (eML) approach. The eML approach fuses the correct and incorrect measurements into a map based on sub-maps generated from each set of measurements. We also propose the maximal sound pressure (mSP) method to detect incorrect sonar readings using the sound pressure of the waves from sonar sensors. In several indoor experiments, integrating the eML approach with the mSP method achieved the best results in terms of map quality among various mapping approaches. We call this the maximum likelihood based on sub-maps (MLS) approach. The MLS map created using only two sonar sensors exhibited similar accuracy to the reference map, which was an accurate representation of the environment.
Keyword:
Grid mapping
Sonar sensor
Maximum likelihood estimation
期刊
IF:
4.3
论文数:
1.7K
被引数:
5.0K
机构
引用论文
Quality measures improving endoscopic screening of colorectal cancer: a review of the literature
PREAI
分享收藏
A practical approach for EKF-SLAM in an indoor environment: fusing ultrasonic sensors and stereo camera.png)
AUTONOMOUS ROBOTS
AUTONOMOUS ROBOTSIF4.3
PREAI
分享收藏On the use of likelihood fields to perform sonar scan matching localizationAUTONOMOUS ROBOTS
AUTONOMOUS ROBOTSIF4.3
PREAI
分享收藏
查看更多内容