Abstract. We propose a novel algorithm for the joint refinement of structure and motion parameters from image data directly without relying on fixed and known correspondences. In contrast to traditional bundle adjustment (BA) where the optimal parameters are determined by minimizing the reprojection error using tracked features, the proposed algorithm relies on maximizing the photometric consistency and estimates the correspondences implicitly. Since the proposed algorithm does not require correspondences, its application is not limited to corner-like structure; any pixel with nonvanishing gradient could be used in the estimation process. Furthermore, we demonstrate the feasibility of refining the motion and structure parameters simultaneously using the photometric in unconstrained scenes and without requiring restrictive assumptions such as planarity. The proposed algorithm is evaluated on range of challenging outdoor datasets, and it is shown to improve upon the accuracy of the state-of-the-art VSLAM methods obtained using the minimization of the reprojection error using traditional BA as well as loop closure.
We propose an automated method to recover the full calibration parameters between a 3D range sensor and a monocular camera system. Our method is not only accurate and fully automated, but also relies on a simple calibration target consisting of a single circle. This allows the algorithm to be suitable for applications requiring in-situ calibration. We demonstrate the effectiveness of the algorithm on a cameralidar system and show results on 3D mapping tasks.
Validating the integrity of pipes is an important task for safe natural gas production and many other operations (e.g. refineries, sewers, etc.). Indeed, there is a growing industry of actuated, actively driven mobile robots that are used to inspect pipes. Many rely on a remote operator to inspect data from a fisheye camera to perform manual inspection and provide no localization or mapping capability. In this work, we introduce a visual odometry-based system using calibrated fisheye imagery and sparse structured lighting to produce high-resolution 3D textured surface models of the inner pipe wall. Our work extends state-of-the-art visual odometry and mapping for fisheye systems to incorporate weak geometric constraints based on prior knowledge of the pipe components into a sparse bundle adjustment framework. These constraints prove essential for obtaining high-accuracy solutions given the limited spatial resolution of the fisheye system and challenging raw imagery. We show that sub-millimeter resolution modeling is viable even in pipes which are 400 mm (16'') in diameter, and that sparse range measurements from a structured lighting solution can be used to avoid the inevitable monocular scale drift. Our results show that practical, high-accuracy pipe mapping from a single fisheye camera is within reach.
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