Commission I, WG I/V KEY WORDS: UAV, Multi-sensor, Rapid Mapping, Real-time Georeferencing ABSTRACT:As the occurrences and scales of disasters and accidents have been increased due to the global warming, the terrorists' attacks, and many other reasons, the demand for rapid responses for the emergent situations also has been thus ever-increasing. These emergency responses are required to be customized to each individual site for more effective management of the emergent situations. These requirements can be satisfied with the decisions based on the spatial changes on the target area, which should be detected immediately or in real-time. Aerial monitoring without human operators is an appropriate means because the emergency areas are usually inaccessible. Therefore, a UAV is a strong candidate as the platform for the aerial monitoring. In addition, the sensory data from the UAV system usually have higher resolution than other system because the system can operate at a lower altitude. If the transmission and processing of the data could be performed in real-time, the spatial changes of the target area can be detected with high spatial and temporal resolution by the UAV rapid mapping systems. As a result, we aim to develop a rapid aerial mapping system based on a UAV, whose key features are the effective acquisition of the sensory data, real-time transmission and processing of the data. In this paper, we will introduce the general concept of our system, including the main features, intermediate results, and explain our real-time sensory data georeferencing algorithm which is a core for prompt generation of the spatial information from the sensory data.

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Nowadays cars are equipped with various built-in sensors such as speedometers, odometers, accelerometers, and gyros for safety and maintenance. Also, front view images can be economically acquired by a low-cost camera available in smartphones or black boxes. The combination of the built-in sensory data and the images can be an effective complement to a GPS based navigation. Therefore, we propose a car navigation framework to determine car position and attitude using the built-in sensory data such as a speed, angular rate and the images from a front view camera. The method consists of three steps, 1) dead reckoning using the velocity and yaw rate provided in real-time, 2) image georeferencing based on a sequential bundle adjustment using the dead reckoning results and 3) final estimation using a Kalman filter with the georeferencing results. The experimental results show that the proposed method can provide the positions with a reasonable accuracy level, which can be meaningful to complement a traditional GPS based navigation with a low cost..

Real-time image georeferencing is essential to the prompt generation of spatial information such as orthoimages from the image sequence acquired by an airborne multi-sensor system. It is mostly based on direct georeferencing using a GPS/INS system, but its accuracy is limited by the quality of the GPS/INS data. More accurate results can be acquired using traditional aerial triangulation (AT) combined with GPS/INS data, which can be performed only as a post-processing method due to intense computational requirements. In this study, we propose a sequential AT algorithm that can produce accurate results comparable to those from the simultaneous AT algorithm in real time. Whenever a new image is added, the proposed algorithm rapidly performs AT with minimal computation at the current stage using the computational results from the previous stage. The experimental results show that the georeferencing of an image sequence at any stage took less than 0.1 s and its accuracy was determined within ± 5 cm on the estimated ground points, which is comparable to the results of simultaneous AT. This algorithm may be used for applications requiring real-time image georeferencing such as disaster monitoring and image-based navigation.

In this paper, we develop a registration method to eliminate the geometric inconsistency between the stereo‐images and light detection and ranging (LIDAR) data obtained by an airborne multisensor system. This method consists of three steps: registration primitive extraction, correspondence establishment, and exterior orientation parameter (EOP) adjustment. As the primitives, we employ object points and linked edges from the stereo‐images and planar patches and intersection edges from the LIDAR data. After extracting these primitives, we establish the correspondence between them, being classified into vertical and horizontal groups. These corresponding pairs are simultaneously incorporated as stochastic constraints into aerial triangulation based on the bundle block adjustment. Finally, the EOPs of the images are adjusted to minimize the inconsistency. The results from the application of our method to real data demonstrate that the inconsistency between both data sets is significantly reduced from the range of 0.5 m to 2 m to less than 0.05 m. Hence, the results show that the proposed method is useful for the data fusion of aerial images and LIDAR data.