Structured light based patterns provide a means to capture the state of an object shape. However it may be inefficient when the object is freely moving, when its surface contains high curvature parts or in out of depth of field situations. For image-based robotic guidance in unstructured and dynamic environment, only one shot is required for capturing the shape of a moving region-of-interest. Then robust patterns and real-time capabilities must be targeted. To this end, we have developed a novel technique for the generation of coded patterns directly driven by the Hamming distance. The counterpart is the big amount of codes the coding/decoding algorithms have to face with a high desired Hamming distance. We show that the mean Hamming distance is a useful criterion for driving the patterns generation process and we give a way to predict its value. Furthermore, to ensure local uniqueness of codewords with consideration of many incomplete ones, the Perfect Map theory is involved. Then, we describe a pseudorandom/exhaustive algorithm to build patterns with more than 200×200 features, in a very short time, thanks to a splitting strategy which performs the Hamming tests in the codeword space instead of the pattern array. This leads to a significant reduction of the computational complexity and it may be applied to other purposes. Finally, real-time reconstructions from single images are reported and results are compared to the best known which are outperformed in many cases.

In this paper, we address a robust matching technique based on coded structured lighting (SL) to achieve real-time 3-D reconstructions. To that purpose, most existing approaches involve color or grey levels coding but they are well-known to be sensitive to spectral properties and texture of the viewed surfaces. Therefore, the overall robustness of the proposed technique comes first from the geometrical features used in conjunction with the SL neighbourhood scheme to carry out pattern coding. Second, a desired minimum Hamming distance between features' codewords drives the pattern design. This is suited for autonomous navigation in unknown environments as this parameter enables codewords correction capabilities during the decoding stage in real-time. Furthermore, we take advantage of the known epipolar geometry by including projective invariants between corresponding epipolar lines directly in the pattern components. Thus, the final pattern displays relocated and reoriented (cuneiform) features along the epipolar lines. This grouping also contributes to reduce the search space more and results in a significantly less constrained coding. Finally, we show how this coding and this non-grid based pattern offer efficient and fast correction of mislabeled features due to blurring, spectral harmful effects and surfaces discontinuities prior to the 3-D reconstruction of real scenes.