High-quality maps are pertinent to performing tasks requiring precision interaction with the environment. Current challenges with creating a high-precision map come from the need for both high pose accuracy and scan accuracy, and the goal of reliable autonomous performance of the task. In this paper, we propose a multistage framework to create a high-precision map of an environment which satisfies the targeted resolution and local accuracy by an autonomous mobile robot. The proposed framework consists of three steps. Each step is intended to aid in resolving the challenges faced by conventional approaches. In order to ensure the pose estimation is performed with high accuracy, a globally accurate coarse map of the environment is created using a conventional technique such as simultaneous localization and mapping or structure from motion with bundle adjustment. The high scan accuracy is ensured by planning a path for the robot to revisit the environment while maintaining a desired distance to all occupied regions. Since the map is to be created with targeted metrics, an online path replanning and pose refinement technique is proposed to autonomously achieve the metrics without compromising the pose and scan accuracy. The proposed framework was first validated on the ability to address the current challenges associated with accuracy through parametric studies of the proposed steps. The autonomous capability of the proposed framework was been demonstrated successfully in its use for a practical mission.
A new design for a hardware system for photometric stereo-based robotic vision is proposed. In addition, a onefactor-at-a-time sensitivity analysis is performed to determine the optimal working distance for varying depths of field and feature depths for the photometric stereo (PS) sensor. The optimal working distance is defined as the distance at which the PS system is able to robustly achieve the best focus measure throughout the entire image. A cubic equation relating focus measure to working distance is found for each feature depth, and it is shown that good focus is achieved within the targeted range of working distances and feature depths. A validation study was also performed to show the results of PS using the designed sensor.
Existing light sectioning (LS) measurement systems are incapable of microtexture road profiling in 10 μm precision though it is needed for the analysis. This paper presents LS measurement enhanced by photometric stereo (PS) for the microtexture road profiling. Since the existing LS measurement systems, traditionally using an industrial CCD camera, suffer from the lack of not only the measurement resolution but also the camera resolution, the proposed LS measurement improves both the resolutions by implementing the PS appropriately: a DSLR camera with a macro lens is used for improving the camera resolution while the PS itself improves the measurement resolution. The surface normals derived from the PS are then incorporated through the Poisson’s integration to allow high-resolution LS measurement. The parametric study first validates the efficacy of the proposed measurement over the conventional LS measurement. Measurements on the actual road have then demonstrated the applicability of the proposed measurement to microtexture road profiling.
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