Pose estimation and map reconstruction are basic requirements for robotic autonomous behavior. In this paper, we propose a point–plane-based method to simultaneously estimate the robot’s poses and reconstruct the current environment’s map using RGB-D cameras. First, we detect and track the point and plane features from color and depth images, and reliable constraints are obtained, even for low-texture scenes. Then, we construct cost functions from these features, and we utilize the plane’s minimal representation to minimize these functions for pose estimation and local map optimization. Furthermore, we extract the Manhattan World (MW) axes on the basis of the plane normals and vanishing directions of parallel lines for the MW scenes, and we add the MW constraint to the point–plane-based cost functions for more accurate pose estimation. The results of experiments on public RGB-D datasets demonstrate the robustness and accuracy of the proposed algorithm for pose estimation and map reconstruction, and we show its advantages compared with alternative methods.

Orientation estimation is a crucial part of robotics tasks such as motion control, autonomous navigation, and 3D mapping. In this paper, we propose a robust visual-based method to estimate robots’ drift-free orientation with RGB-D cameras. First, we detect and track hybrid features (i.e., plane, line, and point) from color and depth images, which provides reliable constraints even in uncharacteristic environments with low texture or no consistent lines. Then, we construct a cost function based on these features and, by minimizing this function, we obtain the accurate rotation matrix of each captured frame with respect to its reference keyframe. Furthermore, we present a vanishing direction-estimation method to extract the Manhattan World (MW) axes; by aligning the current MW axes with the global MW axes, we refine the aforementioned rotation matrix of each keyframe and achieve drift-free orientation. Experiments on public RGB-D datasets demonstrate the robustness and accuracy of the proposed algorithm for orientation estimation. In addition, we have applied our proposed visual compass to pose estimation, and the evaluation on public sequences shows improved accuracy.

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Cell image segmentation is one of the hot topics in medical image processing. Most of the classical cell image segmentation algorithms perform the segmentation directly on the original image and result in the loss of the cell nuclei with low intensity contrast. To solve this problem, this paper presents a novel nuclei segmentation method. Based on analyzing the characteristics of the cell nuclei, we first enhance the nuclei according to their unique features in the image. The proposed nuclei enhancement method combines the intensity and the color information of the image, and is thus effective to enhance the nuclei with relatively low intensity contrast. Then, the morphological reconstruction is employed to extract the regional maxima of the enhanced image, and several shape description parameters are finally used to screen out the true cell nuclei from the extracted regions. Experiments have been performed on real cervical smear images, and the results validate the effectiveness of the proposed method for nuclei segmentation in cervical smear images.Index Terms-Microscopic image processing, nuclei enhancement, nuclei segmentation, morphological reconstruction.

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