In this paper, we describe a method for global localization in a previously unvisited environment using only a schematic floor plan as a prior map. The floor plan need not be a precision map-it can be the sort of image found in buildings to guide people or aid evacuation. The core idea is to identify features that are stable across both a drawn floor plan and robot point-of-view LIDAR data, for example wall intersections, which appear as corners from overhead and as vertical lines from the ground. We introduce a factor graph-based global localization method that uses these features as landmarks. The detections of such descriptorless features are noisy and often ambiguous. We therefore propose robust data association based on a pairwise measurement consistency check and max-mixtures error model. We evaluate the resulting system in a real-world indoor environment, demonstrating performance comparable to a baseline system that uses a conventional LIDAR-based prior map.

For online robots, incremental SLAM algorithms offer huge potential computational savings over batch algorithms. The dominant incremental algorithms are iSAM and iSAM2 which offer radically different approaches to computing incremental updates, balancing issues like 1) the need to re-linearize, 2) changes in the desirable variable marginalization order, and 3) the underlying conceptual approach (i.e. the “matrix” story versus the “factor graph” story). In this paper, we propose a new incremental algorithm that computes solutions with lower absolute error and generally provides lower error solutions for a fixed computational budget than either iSAM or iSAM2. Key to AprilSAM's performance are a new dynamic variable reordering algorithm for fast incremental Cholesky factorizations, a method for reducing the work involved in backsubstitutions, and a new algorithm for deciding between incremental and batch updates.

In GPS-denied environments, robot systems typically revert to navigating with dead-reckoning and relative mapping, accumulating error in their global pose estimate. In this paper, we propose Feature-based Localization between Air and Ground (FLAG), a method for computing global position updates by matching features observed from ground to features in an aerial image. Our method uses stable, descriptorless features associated with vertical structure in the environment around a ground robot in previously unmapped areas, referencing only overhead imagery, without GPS. Multiple-hypothesis data association with a particle filter enables efficient recovery from data association error and odometry uncertainty. We implement a stereo system to demonstrate our vertical feature based global positioning approach in both indoor and outdoor scenarios, and show comparable performance to laser-scan-matching results in both environments.

Time series data are ubiquitous and are of importance in many application problems in engineering, science, medicine, economics and entertainment. Many real world pattern classification problems involve the processing and analysis of multiple variables in the temporal domain. These types of problems are referred to as Multivariate Time Series (MTS) problems. In many real-world applications, an MTS problem can involve a large number of signals, and require algorithms to select signals and extract temporal and spatial features from them. In this paper, we present an innovative convolutional neural network, MTS-DeepNet that is specially designed for MTS pattern classification. The system integrates signal and feature selections with MTS pattern classification in one learning framework. MTS-DeepNet is applied to a real-world problem, namely predicting driver lane departure based on driver's physiological signals. Our experimental results showed that, in comparison to a multi-layer neural network trained with the backpropagation algorithm, MTS-DeepNet gave better prediction accuracy.

This paper presents our research in building a virtual humidity sensor using recurrent Neural Networks. Recurrent Neural Networks are promising methods for the prediction of time series because they provide feedback connections from hidden layer to its inputs and, therefore, can store temporal information learned from previous time steps. This study applies Elman Recurrent Neural Network (ERNN) to forecast the specific humidity from three weather stations. In addition, this study examines the feasibility of applying ERNN in time series forecasting by comparing it with multilayer perceptron network. The experiment results indicate that ERNN is a promising alternative to specific humidity forecasting.

Speed profile prediction on ramps is a challenging problem because speed changes on ramps involve complicated lane maneuvering and frequent acceleration or deceleration depending on geometry of the ramp and traffic volumes. Ramps can be categorized into three groups based on their interconnection of freeway: freeway entering ramps, freeway exit ramps, and inter freeway ramps. However, different geographical shapes of ramps within the same category cause different speed profile distributions. To predict speed profile on any ramp types, we proposed an Intelligent Trip Modeling on Ramp (ITMR) System that consists of a ramp classification method based on the decision tree and speed profile prediction neural networks. The proposed ITMR takes inputs from geographical data on the route and also the personal driving pattern extracted from the knowledge base built with the individual historical driving data. Experimental results show that the proposed system learned dynamic ramp speed changes very well to provide accurate prediction results on multiple freeway entering ramps, exit ramps and inter freeway ramps.