This PDF file contains the front matter associated with SPIE Proceedings Volume 9872, including the Title Page, Copyright information, Table of Contents, and Conference Committee listing.

When an autonomous system has to act in or interact with an environment, a suitable representation of it is required. In the past decades, many different representation forms – especially spacial ones – have been proposed and even more information fusion techniques were developed in order to build these representations from multiple information sources. However, most of these algorithms do not exploit the full potential of the available information. This is caused by the fact that they are not able to handle the full complexity of all possible solutions compatible with the information and that they rely on restrictive assumptions (i.e. independencies) in order to make the computation feasible. In this work, a new methodology is envisioned that utilizes formal methods, in particular solvers for Pseudo-Boolean Optimization, to drop some of these assumptions. In order to illustrate the ideas, information fusion based on belief functions and occupancy grid maps are considered. It is shown that this approach allows for considering dependencies among multiple cells and thus significantly reduces the uncertainty in the resulting representation.

Land cover classification uses multispectral pixel information to separate image regions into categories. Image segmentation seeks to separate image regions into objects and features based on spectral and spatial image properties. However, making sense of complex imagery typically requires identifying image regions that are often a heterogeneous mixture of categories and features that constitute functional semantic units such as industrial, residential, or commercial areas. This requires leveraging both spectral classification and spatial feature extraction synergistically to synthesize such complex but meaningful image units. We present an efficient graphical model for extracting such semantically cohesive regions. We employ an initial hierarchical segmentation of images into features represented as nodes of an attributed graph that represents feature properties as well as their adjacency relations with other features. This provides a framework to group spectrally and structurally diverse features, which are nevertheless semantically cohesive, based on user-driven identifications of features and their contextual relationships in the graph. We propose an efficient method to construct, store, and search an augmented graph that captures nonadjacent vicinity relationships of features. This graph can be used to query for semantic notional units consisting of ontologically diverse features by constraining it to specific query node types and their indicated/desired spatial interaction characteristics. User interaction with, and labeling of, initially segmented and categorized image feature graph can then be used to learn feature (node) and regional (subgraph) ontologies as constraints, and to identify other similar semantic units as connected components of the constraint-pruned augmented graph of a query image.

One major objective of autonomous systems navigating in dynamic environments is gathering information needed for self localization, decision making, and path planning. To account for this, such systems are usually equipped with multiple types of sensors. As these sensors often have a limited field of view and a fixed orientation, the task of active perception breaks down to the problem of calculating alignment sequences which maximize the information gain regarding expected measurements. Action sequences that rotate the system according to the calculated optimal patterns then have to be generated. In this paper we present an approach for calculating these sequences for an autonomous system equipped with multiple sensors. We use a particle filter for multi- sensor fusion and state estimation. The planning task is modeled as a Markov decision process (MDP), where the system decides in each step, what actions to perform next. The optimal control policy, which provides the best action depending on the current estimated state, maximizes the expected cumulative reward. The latter is computed from the expected information gain of all sensors over time using value iteration. The algorithm is applied to a manifold representation of the joint space of rotation and time. We show the performance of the approach in a spacecraft navigation scenario where the information gain is changing over time, caused by the dynamic environment and the continuous movement of the spacecraft

In ISR (Intelligence, Surveillance and Reconnaissance), heterogeneous sources deliver data and information having spatial and temporal attributes. Real time and non-real time data created for various purposes, present in different formats has to be exploited and fused. The Coalition Shared Data (CSD) concept makes the interoperable sharing of ISR data and information possible. The concept itself and a technical approach for it were developed within the multinational projects CAESAR, MAJIIC and MAJIIC 2 and tested in coalition exercises. The interfaces of software systems providing access to CSD data must allow the user to intuitively use the system and form a substantial part with regard to user acceptance. We describe different systems and approaches we designed and developed to access CSD data that can locate and present the data to the user based on his specific demands. Visualization forms an important part of these systems which share the common challenge of representing data and information with spatial and temporal attributes. The visualization of data and information has to be designed in a manner that supports efficient access, discovery and optionally additional processing (such as filtering and sorting). Given the large amount of data and information that may be available, visualization taking into account their quality and inherent uncertainty is an additional challenge. This publication provides an overview of the systems and approaches we developed to present CSD data and identifies challenges common to these systems. To tackle these challenges, we present new research results regarding visualization of data and information with temporal and spatial attributes.

Polaris Sensor Technologies reports on the development of Pedestrian Automated System for Enforcement and Safety (PASES), a radar and video based system used to monitor vehicle and pedestrian traffic with the intent of improving pedestrian safety. Data is fused from a system of multiple sensors and multiple sensor modalities to identify vehicular violations of pedestrian right of way. A focus was placed on the selection of low cost COTS sensors to make the system more widely available to state and local DOTs with limited budgets. Applications include automated enforcement, adaptive traffic control, and improved intersection and crosswalk design based on high quality data available for traffic engineering. We discuss early results with high fidelity sensors, and the performance trades made in order to make the system affordable. A discussion of the system processing architecture is included which highlights the treatment of each sensor data type, and the means of combining the processed data products into state information related to traffic incidents involving vehicles and pedestrians.

The illicit creation of 3D printed keys is problematic as it can allow intruders nearly undetectable access to secure facilities. Prior work has discussed how keys can be created using visible light sensing. This paper builds on this work by evaluating the utility of keys produced with laser scanning. The quality of the model produced using a structured laser scanning approach is compared to the quality of a model produced using a similarly robust visible light sensing approach.

This paper describes the feasibility and implementation of an In-drilling alignment method for an inertia navigation system based measurement while drilling system as applied to the vertical drilling process, where there is seldom direct access to a reference measurement due the communication network architecture and telemetry framework constraints. It involves the sequential measurement update of multiple nodes by the propagation of the reference measurement sequentially over nodes, each of which has an integrated inertia measurement unit sensor and runs the extended Kalman filter based signal processing unit, until the final measurement update of the main sensor node embedded in the borehole assembly is done. This is done particularly to keep track of the yaw angle position during the drilling process.

Structural health prognostics and diagnosis strategies can be classified as either model or signal-based. Artificial neural network strategies are popular signal-based techniques. This paper proposes the use of helicopter blades in order to study the sensitivity of an artificial neural network to structural fatigue. The experimental setup consists of a scale aluminum helicopter blade exposed to transverse vibratory excitation at the hub using single axis electrodynamic shaker. The intent of this study is to optimize an algorithm for processing high-dimensional data while retaining important information content in an effort to select input features and weights, as well as health parameters, for training a neural network. Data from accelerometers and piezoelectric transducers is collected from a known system designated as healthy. Structural damage will be introduced to different blades, which they will be designated as unhealthy. A variety of different tests will be performed to track the evolution and severity of the damage. A number of damage detection and diagnosis strategies will be implemented. A preliminary experiment was performed on aluminum cantilever beams providing a simpler model for implementation and proof of concept. Future work will look at utilizing the detection information as part of a hierarchical control system in order to mitigate structural damage and fatigue. The proposed approach may eliminate massive data storage on board of an aircraft through retaining relevant information only. The control system can then employ the relevant information to intelligently reconfigure adaptive maneuvers to avoid harmful regimes, thus, extending the life of the aircraft.

We are building a cognitive vision system for mobile robots that works in a manner similar to the human vision system, using saccadic, vergence and pursuit movements to extract information from visual input. At each fixation, the system builds a 3D model of a small region, combining information about distance, shape, texture and motion to create a local dynamic spatial model. These local 3D models are composed to create an overall 3D model of the robot and its environment. This approach turns the computer vision problem into a search problem whose goal is the acquisition of sufficient spatial understanding for the robot to succeed at its tasks. The research hypothesis of this work is that the movements of the robot’s cameras are only those that are necessary to build a sufficiently accurate world model for the robot’s current goals. For example, if the goal is to navigate through a room, the model needs to contain any obstacles that would be encountered, giving their approximate positions and sizes. Other information does not need to be rendered into the virtual world, so this approach trades model accuracy for speed.

Performance standards for industrial mobile robots and mobile manipulators (robot arms onboard mobile robots) have only recently begun development. Low cost and standardized measurement techniques are needed to characterize system performance, compare different systems, and to determine if recalibration is required. This paper discusses work at the National Institute of Standards and Technology (NIST) and within the ASTM Committee F45 on Driverless Automatic Guided Industrial Vehicles. This includes standards for both terminology, F45.91, and for navigation performance test methods, F45.02. The paper defines terms that are being considered. Additionally, the paper describes navigation test methods that are near ballot and docking test methods being designed for consideration within F45.02. This includes the use of low cost artifacts that can provide alternatives to using relatively expensive measurement systems.

In this paper the system of wireless transmission of data based on the use an adaptive algorithm for processing spatial-time signals using antenna arrays is presented. In the transmission of data in a multipath propagation of signals have been used such technologies as a MIMO (Multiple input-Multiple output) and OFDM (Orthogonal frequency division multiplexing) to solve the problem of increasing the maximum speed of data transfer and the low probability of errors. The adaptation process is based on the formation of the directional pattern equivalent to the amplitude antenna array in the signal arrival direction with the highest capacity on one of propagation paths in the channel. The simulation results showed that the use of an adaptive algorithm on the reception side can significantly reduce the probability of bit errors, thus to increase throughput in an underwater acoustic data channel.