RONet: Realtime Rangeonly Indoor Localization via Stacked Bidirectional LSTM with Residual Attention In this study, a threelayered bidirectional Long Shortterm Memory (Bixe2x80x93LSTM) with residual attention, named as RONet, is proposed to achieve localization using range measurements. Accordingly, we acquired our own datasets and tested RONet using realistic conditions. It is shown that the RONet can estimate the position of the mobile robot in real time using the Nvidia Jetson AGX Xavier based only on range measurements. We also analyzed the sequence length of LSTM as a type of hyperparameters. We found that optimal sequence length is eight for more than eight anchors and twelve for fewer anchors compared to sequences with different lengths, given that construction of the network with the optimal sequence length estimates the position precisely and accounts for uncertainties. As verified experimentally, RONet yields more precise performance and results in increased robustness against outliers compared to a conventional rangeonly approach based on a particle filtering and the other conventional deeplearningbased approaches. We set three cases, reduced the number of anchors, and verified that the RONet was a robust solution. We also confirmed that it is the best solution that yields the smallest RootMeanSquareError (RMSE) values, equal to 4.466 cm, 3.210 cm, and 3.090 cm, in the cases where three, five, and eight anchors were deployed, respectively.

An LSTM Network for RealTime Odometry Estimation The use of 2D laser scanners is attractive for the autonomous driving industry because of its accuracy, lightweight and lowcost. However, since only a 2D slice of the surrounding environment is detected at each scan, it is a challenge to execute important tasks such as the localization of the vehicle. In this paper we present a novel framework that explores the use of deep Recurrent Convolutional Neural Networks (RCNN) for odometry estimation using only 2D laser scanners. The application of RCNNs provides the tools to not only extract the features of the laser scanner data using Convolutional Neural Networks (CNNs), but in addition it models the possible connections among consecutive scans using the Long ShortTerm Memory (LSTM) Recurrent Neural Network. Results on a real road dataset show that the method can run in realtime without using GPU acceleration and have competitive performance compared to other methods, being an interesting approach that could complement traditional localization systems.