Prevention 0f Unwanted Free-Declaration of Static Obstacles in Probability Occupancy Grids

2017 International Conference on Artificial Intelligence Applications and Technologies

计算机科学

Krause, Stefan^1 ; Scholz, M.^2 ; Hohmann, R.^1

DLR Institute of Flight Systems, Department of Unmanned Aircraft, German Aerospace Center, Germany^1

DLR Institute of Transportation Systems, Department of Data Management and Knowledge Discovery, Braunschweig

38108, Germany^2

关键词: Continuous lasers;    Continuous measurements;    Obstacle detection;    Probabilistic grid maps;    Processing performance;    Real environments;    Spatial resolution;    Unmanned aviation;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/261/1/012013/pdf
DOI  :  10.1088/1757-899X/261/1/012013

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

Obstacle detection and avoidance are major research fields in unmanned aviation. Map based obstacle detection approaches often use discrete world representations such as probabilistic grid maps to fuse incremental environment data from different views or sensors to build a comprehensive representation. The integration of continuous measurements into a discrete representation can result in rounding errors which, in turn, leads to differences between the artificial model and real environment. The cause of these deviations is a low spatial resolution of the world representation comparison to the used sensor data. Differences between artificial representations which are used for path planning or obstacle avoidance and the real world can lead to unexpected behavior up to collisions with unmapped obstacles. This paper presents three approaches to the treatment of errors that can occur during the integration of continuous laser measurement in the discrete probabilistic grid. Further, the quality of the error prevention and the processing performance are compared with real sensor data.