Morgan Ulvklo

An information system for target recognition

We present an approach to a general decision support system. The aim is to cover the complete process for automatic target recognition, from sensor data to the user interface. The approach is based on a query-based information system, and include tasks like feature extraction from sensor data, data association, data fusion and situation analysis. Currently, we are working with data from laser radar, infrared cameras, and visual cameras, studying target recognition from cooperating sensors on one or several platforms. The sensors are typically airborne and at low altitude. The processing of sensor data is performed in two steps. First, several attributes are estimated from the (unknown but detected) target. The attributes include orientation, size, speed, temperature etc. These estimates are used to select the models of interest in the matching step, where the target is matched with a number of target models, returning a likelihood value for each model. Several methods and sensor data types are used in both steps. The user communicates with the system via a visual user interface, where, for instance, the user can mark an area on a map and ask for hostile vehicles in the chosen area. The user input is converted to a query in ΣQL, a query language developed for this type of applications, and an ontological system decides which algorithms should be invoked and which sensor data should be used. The output from the sensors is fused by a fusion module and answers are given back to the user. The user does not need to have any detailed technical knowledge about the sensors (or which sensors that are available), and new sensors and algorithms can easily be plugged into the system.

Concurrent Path and Sensor Planning for a UAV - Towards an Information Based Approach Incorporating Models of Environment and Sensor

In this paper we propose a framework for autonomous path and sensor planning of a UAV performing a surveillance and exploration mission. Concurrent path and sensor planning is a very challenging problem, since realistic models of platforms, sensors and environment are very complex and inherently non-linear. Our approach is one attempt to address these issues and formulate an optimization problem using tools from information theory

Image processing and sensor management for autonomous UAV surveillance

This paper describes a framework for image processing and sensor management for an autonomous unmanned airborne surveillance system equipped with infrared and video sensors. Our working hypothesis is that integration of the detection-tracking-classification chain with spatial awareness makes possible intelligent autonomous data acquisition by means of active sensor control. A central part of the framework is a surveillance scene representation, suitable for target tracking, geolocation, and sensor data fusion involving multiple platforms. The representation, based on Simultaneous Localization and Mapping, SLAM, take into account uncertainties associated with sensor data, platform navigation, and prior knowledge. A client/server approach, for on-line adaptable surveillance missions, is introduced. The presented system is designed to simultaneously and autonomously perform the following tasks: provide wide area coverage from multiple viewpoints by means of a step-stare procedure, detect and track multiple stationary and moving ground targets, perform a detailed analysis of detected regions-of-interest, and generate precise target coordinates by means of multi-view geolocation techniques.

A sensor management framework for autonomous UAV surveillance

This paper presents components of a sensor management architecture for autonomous UAV systems equipped with IR and video sensors, focusing on two main areas. Firstly, a framework inspired by optimal control and information theory is presented for concurrent path and sensor planning. Secondly, a method for visual landmark selection and recognition is presented. The latter is intended to be used within a SLAM (Simultaneous Localization and Mapping) architecture for visual navigation. Results are presented on both simulated and real sensor data, the latter from the MASP system (Modular Airborne Sensor Platform), an in-house developed UAV surrogate system containing a gimballed IR camera, a video sensor, and an integrated high performance navigation system.

Adaptive reconstruction using multiple views

This paper introduces a novel algorithm for extracting the optical flow obtained from a translating camera in a static scene. Occlusion between objects is incorporated as a natural component in a scene reconstruction strategy by first evaluating and reconstructing the foreground and then excluding its influence on the partly occluded objects behind.