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- W2897459191 abstract "This paper proposes a method for estimation of the reliability of vehicle localization results. We previously proposed a fault detection method for indoor mobile robots using a convolutional neural network (CNN). Because image data is generally fed to a CNN, we feed image data obtained from the robot pose, occupancy grid map, and laser scan data to the CNN, which decides of whether localization has failed. The previous method also employed a Rao-Blackwellized particle filter to estimate the robot pose and reliability of this estimation simultaneously. However, it was difficult for vehicle robots to use the previous method as creating and processing image data is not a light computation process. In this study, we extend the previous method by improving the data fed to the CNN, thus making it possible for vehicle robots to perform simultaneous localization and estimation. This paper describes in detail the simultaneous estimation and shows that the reliability can be used as an exact criterion for detecting localization failures. Keywords-Vehicle Localization, Reliability" @default.
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- W2897459191 date "2018-06-01" @default.
- W2897459191 modified "2023-10-16" @default.
- W2897459191 title "Reliability Estimation of Vehicle Localization Result" @default.
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- W2897459191 doi "https://doi.org/10.1109/ivs.2018.8500625" @default.
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