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.

Solving visual features' correspondence and dealing with missing data are two factors of limitations for points registration techniques. To tackle this problem, we conceived a pattern, primarily designed for structured lighting vision systems, which may also be used for camera calibration purposes. The pattern design previously presented provides a huge of benefits. Among them, we firstly present a new calibration technique of a structured lighting system and secondly an automatic distortion compensation based on a printed pattern. These two well-known issues are very useful in 3D vision-based metrology with range data, for instance for model-based visual robot control, especially when the model is incrementally built with a real-time 3D reconstruction of moving surfaces. Perhaps, one of the most significant profit with a high Hamming distance pattern is the ability to reliably decode its projected individual elements even if several of items are missing, as it greatly extends the range of measurements volume. A technique which solves the distortion parameters by means of a robust M-estimator algorithm is presented. It uses a printed pattern and it allows the distortion be corrected with a single view and without the computation of other (intrinsic/extrinsic) parameters, even in presence of occlusions. Experimental results, in one hand by means of a printed pattern for the distortion compensation of a rigid endoscope and on the other hand by means of a projected pattern for the calibration of the structured lighting system, show very good performance for the 3-D reconstruction.

The development of new medical therapies often require experiments on small animals. In order to improve the medical protocol during treatments with needles, we propose a new robotic needle insertion system using CT-scan imaging and visual servoing. The biologist defines the skin entry point and the target to be reached in the CT-image. The needle target is then expressed in the robot frame thanks to a 3D registration between the 3D structured light reconstruction of the animal bed and the 3D model obtained from the CT-scan data. Finally the needle pointing is performed thanks to a 2D visual servoing. This approach is independent of the needle model and allows a high positioning accuracy: less than 0.5 pixel for the tip of the needle and less than 10-3rad for the direction. Experiments show promising results: a needle with a diameter of 0.72 mm can be inserted through two holes with diameters of 2 mm.

In this paper we present a new monochromatic pattern for a robust structured light coding based on the spatial neighborhood scheme using the M-array approach. The proposed pattern is robust as it allows a high error rate characterized by an average Hamming distance higher than 6. We tackle the design problem with the definition of a small set of symbols associated to simple geometrical primitives. One of these primitives embeds the local orientation of the pattern. This is helpful while performing the search for the relevant neighborhood during the decoding process. The aim of this work is to use this pattern for the real-time 3-D reconstruction of dynamic scenes, particularly in endoscopic surgery, with fast and reliable detection and decoding stages. Ongoing results are presented to assess both the capabilities of the proposed pattern and the reliable decoding algorithm with projections onto simple 3-D scenes and onto internal structures of a pig abdomen.

In this paper we present a new pattern for the robust coding of the structured light based on the spatial neighborhood scheme and by means of the M-array approach. The pattern is robust as it allows a high error rate characterized by a Hamming distance higher than 3. We tackle the design problem with the definition of a small set of symbols associated to geometrical features with simple shapes. We benefit from the stripe, as it is one of the considered primitives, to embed the local orientation of the pattern. This is helpful while performing the search for the relevant neighborhood during the decoding process. The aim of this work is to use this pattern for the 3 - D reconstruction of dynamic scenes with fast and reliable detection and decoding stages. Ongoing results are presented to assess both the capabilities of the proposed pattern and the decoding algorithm with projections onto simple 3 - D scenes.