Automobiles or robots with advanced autonomous systems are equipped with multiple types of sensors to overcome different weather and geographical conditions. These sensors generally have various data delays and sampling rates. Additionally, the communication delays or time synchronization errors between the onboard computers significantly affect the robustness and accuracy of localization for autonomous vehicles. In this paper, the simultaneous estimation of vehicle position and sensor delays using a Gaussian process based moving horizon estimation (GP-MHE) is presented. The GP-MHE can estimate the unknown delays of multiple sensors with the resolution less than that of GP-MHE sampling rate. The localization performance of GP-MHE was confirmed using full-vehicle simulator, then evaluated in a real vehicle experiment on a highway scenario. Experimental result verified the sufficient localization accuracy of sub 0.3m using data that had irregular sampling rate and delay of more than 150ms. The proposed algorithm extends the capability of integrating various data with large unknown delays for vehicles, robots, drones and remote autonomy.

Motivation: Multi-dimensional NMR spectra are generally used for NMR signal assignment and structure analysis. There are several programs that can achieve highly automated NMR signal assignments and structure analysis. On the other hand, NMR spectra tend to have a large number of noise peaks even for data acquired with good sample and machine conditions, and it is still difficult to eliminate these noise peaks. Results: We have developed a method to eliminate noise peaks using convolutional neural networks, implemented in the program package Filt_Robot. The filtering accuracy of Filt_Robot was around 90-95% when applied to 2D and 3D NMR spectra, and the numbers of resulting non-noise peaks were close to those in corresponding manually prepared peaks lists. The filtering can strongly enhance automated NMR spectra analysis. Availability and implementation: The full package of the program, documents and example data are available from http://bmrbdep.pdbj.org/en/nmr_tool_box/Filt_Robot.html. Supplementary information: Supplementary data are available at Bioinformatics online.