Inspection of deep tunnel networks is extremely challenging due to their inaccessibility, and them being an unknown and potentially hazardous environment. Unmanned aerial vehicles (UAVs) provide a viable alternative for access, and are unaffected by debris or sewer flow. However, commercial UAVs are designed for high altitude aerial imagery and are not appropriate for short-range detailed imaging of tunnel surfaces. In addition, autonomous flight is usually achieved using GPS, which is not available underground. This paper presents the design and development of a smart UAV platform, Surveyor with Intelligent Rotating Lens (SWIRL), customized for autonomous operation in tunnels. It can capture high resolution images for subsequent image processing, and defect detection and classification. An innovative rotating system enables undistorted imaging of the tunnel's inner circumference surface using a single camera. The proposed location method using limited data resulted in substantial unit weight and power consumption reductions, compared to existing systems, making more than 35 minutes of autonomous flight possible.
For fast-moving event cameras, projection of events onto the image frame exhibits smearing of events analogous to high motion blur. For camera attitude estimation, this presents a causality dilemma where motion prior is required to unsmear events, but an image prior is required to estimate motion. This dilemma is typically circumvented by including an IMU to provide motion priors. However, IMUs limited dynamic range of ±2000 • /s are shown to be insufficient for high angular rate rotorcrafts. Contrast Maximization is an event-only optimization framework that computes the optimal motion compensation parameter while generating an event image simultaneously. This paper analyses the performance of existing aggregation functions of the contrast maximization framework and proposes a nonconvolution-based aggregation function that outperforms existing implementations. The use of discrete event images for optimizers is discussed, demonstrating alternate avenues of the framework to exploit. The effect of motion blur in motion-compensated images is defined and studied for Contrast Maximisation at high angular rates. Lastly, the framework is applied to rotation datasets with angular rates exceeding 2000 • /s to demonstrate high angular rate motion estimation without motion priors.
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