We present a workflow for seamless real-time navigation and 3D thermal mapping in combined indoor and outdoor environments in a global reference frame. The automated workflow and partly real-time capabilities are of special interest for inspection tasks and also for other time-critical applications. We use a hand-held integrated positioning system (IPS), which is a real-time capable visual-aided inertial navigation technology, and augment it with an additional passive thermal infrared camera and global referencing capabilities. The global reference is realized through surveyed optical markers (AprilTags). Due to the sensor data’s fusion of the stereo camera and the thermal images, the resulting georeferenced 3D point cloud is enriched with thermal intensity values. A challenging calibration approach is used to geometrically calibrate and pixel-co-register the trifocal camera system. By fusing the terrestrial dataset with additional geographic information from an unmanned aerial vehicle, we gain a complete building hull point cloud and automatically reconstruct a semantic 3D model. A single-family house with surroundings in the village of Morschenich near the city of Jülich (German federal state North Rhine-Westphalia) was used as a test site to demonstrate our workflow. The presented work is a step towards automated building information modeling.

Autonomous vehicles and robots require increasingly more robustness and reliability to meet the demands of modern tasks. These requirements specially apply to cameras onboard such vehicles because they are the predominant sensors to acquire information about the environment and support actions. Cameras must maintain proper functionality and take automatic countermeasures if necessary. Existing solutions are typically tailored to specific problems or detached from the downstream computer vision tasks of the machines, which, however, determine the requirements on the quality of the produced camera images. We propose a generic and task-oriented self-health-maintenance framework for cameras based on data-and physically-grounded models. To this end, we determine two reliable, real-time capable estimators for typical image effects of a camera in poor condition (blur, noise phenomena and most common combinations) by evaluating traditional and customized machine learning-based approaches in extensive experiments. Furthermore, we implement the framework on a real-world ground vehicle and demonstrate how a camera can adjust its parameters to counter an identified poor condition to achieve optimal application capability based on experimental (non-linear and non-monotonic) input-output performance curves. Object detection is chosen as target application, and the image effects motion blur and sensor noise as conditioning examples. Our framework not only provides a practical ready-to-use solution to monitor and maintain the health of cameras, but can also serve as a basis for extensions to tackle more sophisticated problems that combine additional data sources (e.g., sensor or environment parameters) empirically in order to attain fully reliable and robust machines.

The ability to test methods based on simulated data is an important component in the development of intelligent localization systems at the present time. This includes the replication of realistic environments and sensors on the one hand. On the other hand, an implementation of a correct movement profile of the target mobile platform is required. This is complicated by the large variety of possible platform variants. We propose a method to transfer movement profiles that have been recorded in real world into a simulation environment. The associated modular simulation framework was designed especially, but not limiting, for the development of visual-inertial odometry of a handheld navigation system. We demonstrate the application of our pipeline through an experiment to determine the influence of motion blur on the visual odometry component.