Tactical situational awareness for military applications should be based on infrastructure-free systems and should be able to form knowledge of the previously unknown environment. Also, information of the soldier’s context is important for successful operations, e.g. if the soldier is running, crawling or static for a long time.
Requirements for the system are stringent; it should function also in indoor environments, be lightweight and inexpensive. The infrastructure-free requirement is motivated by the fact that rescue and military personnel must be able to operate reliably in any environment, regardless of the available infrastructure.

Test setup and decapitated sensor set in use

Simultaneous Localization and Mapping (SLAM) is a key technology for providing an accurate and reliable infrastructure-free solution for indoor situational awareness. However, indoor environments and the requirements for the system make the implementation of SLAM using existing algorithms challenging. Due to the limitations set by the application, we will implement SLAM using a monocular camera as an input. However, existing algorithms formonocular SLAM do not provide reliable enough results for rescue or military applications. Our approach is to integrate methods previously developed by the research group (Ruotsalainen L. 2013, Vision-aided pedestrian navigation for challenging GNSS environments, doctoral dissertation) for existing SLAM algorithms for improved performance.

At present, most functioning indoor localization systems are based on processing short- range radio signals from pre-installed networks and therefore cannot be considered as infrastructure-free. Advances in sensor technology have been rapid during the last several decades. Self-contained Micro-Electro- Mechanical (MEMS) sensors fulfill the size and cost requirements set for an infrastructure-free military and rescue system. Use of, e.g., inertial sensors provides enough information for propagating a known initial position for the purposes of forming a SLAM solution with a camera. However, the MEMS sensors suffer from biases and drift errors that may decrease the position accuracy substantially. Therefore, sophisticated error modelling and implementation of integration algorithms are key for providing a viable final result.

SONAR and Gopro: equipment needed in INTACT

Integration of different sensors has been an active research area already for some years, but there does not yet exist an accurate and reliable infrastructure-free indoor positioning system. Our approach is to integrate a monocular camera, multiple Inertial Measurement Units (IMUs), a barometer and a ranging sensor to obtain a solution for SLAM, as well as tactical motion information. This project investigates also some sensors less used for positioning, such as ultrasound, for obtaining more accurate positioning and also resolving the height of the camera, which is needed for the visual processing of the method discussed below. Also, positioning using multiple inertial sensors is studied. One inertial unit is foot mounted and other units will be placed on the helmet and body enabling context recognition, e.g. observing the dynamics of the soldier or if he has been static for a long time and therefore possibly wounded.

Most important research questions are:

– How an accurate and reliable SLAM system may be obtained using a single camera, multiple inertial sensors and ranging equipment

– How good situational awareness the equipment provides

The research investigates also some sensors, previously less used for indoor positioning but suitable for the task, such as ultrasound ranging equipment attached to the soldier’s person and digital tv signals, whose antenna may also be attached to soldier’s equipment.


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