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• W2034626455 abstract "The ability to simultaneously leverage multiple modes of sensor information is critical for perception of an automated vehicle's physical surroundings. Spatio-temporal alignment of registration of the incoming information is often a prerequisite to analyzing the fused data. The persistence and reliability of multi-modal registration is therefore the key to the stability of decision support systems ingesting the fused information. LiDAR-video systems like on those many driverless cars are a common example of where keeping the LiDAR and video channels registered to common physical features is important. We develop a deep learning method that takes multiple channels of heterogeneous data, to detect the misalignment of the LiDAR-video inputs. A number of variations were tested on the Ford LiDAR-video driving test data set and will be discussed. To the best of our knowledge the use of multi-modal deep convolutional neural networks for dynamic real-time LiDAR-video registration has not been presented." @default.
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• W2034626455 date "2015-09-01" @default.
• W2034626455 modified "2023-10-12" @default.
• W2034626455 title "Multi-modal sensor registration for vehicle perception via deep neural networks" @default.
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• W2034626455 doi "https://doi.org/10.1109/hpec.2015.7322485" @default.
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