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• W2513401428 abstract "Obstacle fusion algorithms usually perform obstacle association and gating in order to improve the obstacle position if it was detected by multiple sensors. However, this strategy is not common in multi sensor occupancy grid fusion. Thus, the quality of the fused grid, in terms of obstacle position accuracy, largely depends on the sensor with the lowest accuracy. In this paper an efficient method to associate obstacles across sensor grids is proposed. Imprecise sensors are discounted locally in cells where a more accurate sensor, that detected the same obstacle, derived free space. Furthermore, fixed discount factors to optimize false negative and false positive rates are used. Because of its generic formulation with the covariance of each sensor grid, the method is scalable to any sensor setup. The quantitative evaluation with a highly precise navigation map shows an increased obstacle position accuracy compared to standard evidential occupancy grid fusion." @default.
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• W2513401428 date "2016-06-01" @default.
• W2513401428 modified "2023-09-27" @default.
• W2513401428 title "Locally adaptive discounting in multi sensor occupancy grid fusion" @default.
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• W2513401428 doi "https://doi.org/10.1109/ivs.2016.7535396" @default.
• W2513401428 hasPublicationYear "2016" @default.
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