Taking selfies has become a photographic trend nowadays. We envision the emergence of the "video selfie" capturing a short continuous video clip (or burst photography) of the user, themselves. A selfie stick is usually used, whereby a camera is mounted on a stick for taking selfie photos. In this scenario, we observe that the camera typically goes through a special trajectory along a sphere surface. Motivated by this observation, in this work, we propose an efficient and globally optimal relative camera pose estimation between a pair of two images captured by a camera mounted on a selfie stick. We exploit the special geometric structure of the camera motion constrained by a selfie stick and define its motion as spherical joint motion. By the new parametrization and calibration scheme, we show that the pose estimation problem can be reduced to a 3-DoF (degrees of freedom) search problem, instead of a generic 6-DoF problem. This allows us to derive a fast branch-and-bound global optimization, which guarantees a global optimum. Thereby, we achieve efficient and robust estimation even in the presence of outliers. By experiments on both synthetic and real-world data, we validate the performance as well as the guaranteed optimality of the proposed method.

One of the core applications of light field imaging is depth estimation. To acquire a depth map, existing approaches apply a single photo-consistency measure to an entire light field. However, this is not an optimal choice because of the non-uniform light field degradations produced by limitations in the hardware design. In this paper, we introduce a pipeline that automatically determines the best configuration for photo-consistency measure, which leads to the most reliable depth label from the light field. We analyzed the practical factors affecting degradation in lenslet light field cameras, and designed a learning based framework that can retrieve the best cost measure and optimal depth label. To enhance the reliability of our method, we augmented an existing light field benchmark to simulate realistic source dependent noise, aberrations, and vignetting artifacts. The augmented dataset was used for the training and validation of the proposed approach. Our method was competitive with several state-of-the-art methods for the benchmark and real-world light field datasets.

We present a novel method for the geometric calibration of micro-lens-based light field cameras. Accurate geometric calibration is the basis of various applications. Instead of using sub-aperture images, we directly utilize raw images for calibration. We select appropriate regions in raw images and extract line features from micro-lens images in those regions. For the entire process, we formulate a new projection model of a micro-lens-based light field camera, which contains a smaller number of parameters than previous models. The model is transformed into a linear form using line features. We compute the initial solution of both the intrinsic and the extrinsic parameters by a linear computation and refine them via non-linear optimization. Experimental results demonstrate the accuracy of the correspondences between rays and pixels in raw images, as estimated by the proposed method.

This paper presents a vision system and a depth processing algorithm for DRC-HUBO+, the winner of the DRC finals 2015. Our system is designed to reliably capture 3D information of a scene and objects and to be robust to challenging environment conditions. We also propose a depth-map upsampling method that produces an outliers-free depth map by explicitly handling depth outliers. Our system is suitable for robotic applications in which a robot interacts with the real-world, requiring accurate object detection and pose estimation. We evaluate our depth processing algorithm in comparison with state-of-the-art algorithms on several synthetic and real-world datasets.

We present an object proposal method which utilizes the 3D data obtained from a depth sensor as well as the color information of images. Our object proposal method is designed to improve the performance of the object detection for a mobile robot equipped with a camera and a laser scanner. Compared to traditional object proposal methods using only 2D images, the proposed method provides much less number of candidate windows for object detection. We show less than 100 object proposal windows per image using the proposed method result in high recall tested on the public dataset. Our method presents object proposals in 3D space as well as in 2D image thus it can further be applied to following tasks for mobile robots such as 3D location and pose estimation of the target object after successful object detection. We validate our method using the real-world object detection dataset for outdoor mobile robots captured during the DRC Finals 2015 and the public dataset for comparison with the previous methods.

This paper describes a new methodology for estimating the relative pose between two 2-D lidars. Scanned points of 2-D lidars do not have enough feature information for correspondence matching. For this reason, additional image sensors or artificial landmarks at known locations have been used to find the relative pose. We propose a novel method of estimating the relative pose between 2-D lidars without any additional sensors or artificial landmarks. By scanning two orthogonal planes, we utilize the coplanarity of the scan points on each plane and the orthogonality of the plane normals. Even if we capture planes which are not exactly orthogonal, the method provides good results using nonlinear optimization. Experiments with both synthetic and real data show the validity of the proposed method. We also derive two degenerate cases: one related to plane poses, and the other caused by the relative pose. To the best of our knowledge, this study provides the first solution for the problem.

This paper introduces an algorithm that accurately estimates depth maps using a lenslet light field camera. The proposed algorithm estimates the multi-view stereo correspondences with sub-pixel accuracy using the cost volume. The foundation for constructing accurate costs is threefold. First, the sub-aperture images are displaced using the phase shift theorem. Second, the gradient costs are adaptively aggregated using the angular coordinates of the light field. Third, the feature correspondences between the sub-aperture images are used as additional constraints. With the cost volume, the multi-label optimization propagates and corrects the depth map in the weak texture regions. Finally, the local depth map is iteratively refined through fitting the local quadratic function to estimate a non-discrete depth map. Because micro-lens images contain unexpected distortions, a method is also proposed that corrects this error. The effectiveness of the proposed algorithm is demonstrated through challenging real world examples and including comparisons with the performance of advanced depth estimation algorithms.

We propose a novel camera calibration method for defocused images using a smartphone under the assumption that the defocus blur is modeled as a convolution of a sharp image with a Gaussian point spread function (PSF). In contrast to existing calibration approaches which require well-focused images, the proposed method achieves accurate camera calibration with severely defocused images. This robustness to defocus is due to the proposed set of unidirectional binary patterns, which simplifies 2D Gaussian deconvolution to a 1D Gaussian deconvolution problem with multiple observations. By capturing the set of patterns consecutively displayed on a smartphone, we formulate the feature extraction as a deconvolution problem to estimate feature point locations in sub-pixel accuracy and the blur kernel in each location. We also compensate the error in camera parameters due to refraction of the glass panel of the display device. We evaluate the performance of the proposed method on synthetic and real data. Even under severe defocus, our method shows accurate camera calibration result.

This paper describes a new methodology for estimating a relative pose of two 2D laser sensors. Two dimensional laser scan points do not have enough feature information for motion tracking. For this reason, additional image sensors or artificial landmarks have been used to find a relative pose. We propose the method to estimate a relative pose of 2D laser sensors without any additional sensor or artificial landmark. By scanning two orthogonal planes, we utilize only the coplanarity of the scan points on each plane and the orthogonality of the plane normals. Experiments with both synthetic and real data show the validity of the proposed method. To the best of our knowledge this works provides the first solution for the problem.

In this paper are presented simple and practical solutions to extrinsic calibration between a camera and a 2D laser sensor, without overlap. Previous methods utilized a plane or an intersecting line of two planes as a geometric constraint with enough common field-of-view. These required additional sensors to calibrate non-overlapping systems. In this paper, we present two methods for solving the problem - one utilizes a plane; the other utilizes an intersecting line of two planes. For each method, an initial solution of the relative positions of a non-overlapping camera and a laser sensor, was computed by adopting a reasonable assumption about geometric structures. Then we refined it via non-linear optimization, even if the assumption was not perfectly satisfied. Both simulation results and experiments using real data showed that the proposed methods provided reliable results compared to ground-truth, and similar or better results than those provided by a conventional method.

This paper presents a new structure-from-motion (SFM) technique called `generalized laser three-point' algorithm. It is designed to estimate the motion of the camera-laser fusion system which consists of a 2D laser sensor and multiple cameras. The laser points are projected onto the images and tracked to other frames to be used as 3D-2D correspondences. However, the typical three-point algorithms cannot estimate the motion of the system if three points are collinear. Using the laser points as 3D points, this case happens frequently if the laser sensor scans a large plane (e.g. open ground). Even in that case, two frames of the laser data are not collinear if the system is moved while it captures the frames. Among three point correspondences required to estimate the motion, we select two points and the other point from different frames. We estimate the relative pose between the frames by solving an 8-degree polynomial equation. The experimental results show that the proposed algorithm is more appropriate for our fusion system than the previous algorithms.

Building maps of unknown environments is a critical factor for autonomous navigation and homing, and this problem is especially challenging in large-scale environments. Recently, sensor fusion systems such as combinations of cameras and laser sensors have become popular in the effort to ensure a general level of performance in this task. In this paper, we present a new homing method in a large-scale environment using a laser-camera fusion system. Instead of fusing data to form a single map builder, we adaptively select sensor data to handle environments which contain ambiguity. For autonomous homing, we propose a new mapping strategy for building a hybrid map and a return strategy for selecting the next target waypoints efficiently. The experimental results demonstrate that the proposed algorithm enables the autonomous homing of a robot in a large-scale indoor environments in real time.

In this paper, we propose an automatic method to extract a foreground object captured from multiple viewpoints. We consider the foreground object is within the visual hull of camera field of views. By exploring the multi-view geometric relationship and color measurements of the input images, we can estimate the foreground segmentations as well as their fractional boundaries. To facilitate efficient computation and high quality mattes, we adopt a two-phase approach. The first phase of our algorithm provides quick and rough binary segmentations of the foreground object using graph-cut; the second phase refines the segmentation boundaries using matting. Our result is the high quality alpha mattes of the foreground object consistently across all different viewpoints. We demonstrate the effectiveness of our method using challenging examples.

In this paper, we present an accurate and robust 6D SLAM method that uses multiple 2D sensors, i.e. perspective cameras and planar laser scanners. We have investigated strengths and weaknesses of those two sensors for 6D SLAM by conducting specifically designed experiments, and found that the sensors can complement each other. In order to take full advantages of each approach, we fuse correspondences of those two sensors, rather than individually estimated motions. Correspondences obtained by the two sensors have different characteristics, but can be expressed in a common 2D-3D relation form. We use the correspondences in a single structure from-motion framework. In the initial motion estimation step, we propose a RANSAC-based method to generate and test multiple motion hypotheses by using multiple pools of correspondences, aiming to avoid potential bias of each sensor data. In the later motion refinement step, we introduce a variant of bundle adjustment to consider different types of constraints from the two sensors. The performance of the proposed method is demonstrated both quantitatively by experiments on closed loop sequences and qualitatively by large-scale experiments with DGPS trajectory. The proposed method successfully closes a loop of 320 meters in twenty thousand frames by incremental process only.

In this paper, we present a sensor fusion system of cameras and 2D laser sensors for 3D reconstruction. The proposed system is designed to capture data on a fast-moving ground vehicle. The system consists of six cameras and one 2D laser sensor. In order to capture data at high speed, we synchronized all sensors by detecting the laser ray at a specific angle and generating a trigger signal for the cameras. Reconstruction of 3D structures is done by estimating frame-by-frame motion and accumulating vertical laser scans. The difference between the proposed system and the previous works using two 2D laser sensors is that we do not assume 2D motion. The motion of the system in 3D space (including absolute scale) is estimated accurately by data-level fusion of images and range data. The problem of error accumulation is solved by loop closing, not by GPS. The moving objects are detected by utilizing the depth information provided by the laser sensor. The experimental results show that the estimated path is successfully overlayed on the satellite images.

Recently, many vision-based navigation methods have been introduced as an intelligent robot application. However, many of these methods mainly focus on finding an image in the database corresponding to a query image. Thus, if the environment changes, for example, objects moving in the environment, a robot is unlikely to find consistent corresponding points with one of the database images. To handle these problems, we propose a novel motion-based navigation method in contrast with appearance-based approaches. This algorithm is based on motion estimation by a camera to plan the next movement of a robot and robust feature matching to recognize home and destination locations. Experimental results demonstrate the capability of the vision-based autonomous navigation against environment changes.

The CCD camera and the 2D laser range finder are widely used for motion estimation and 3D reconstruction. With their own strengths and weaknesses, low-level fusion of these two sensors complements each other. We combine these two sensors to perform motion estimation and 3D reconstruction simultaneously and precisely. We develop a motion estimation scheme appropriate for this sensor system. In the proposed method, the motion between two frames is estimated using three points among the scan data, and refined by nonlinear optimization. We validate the accuracy of the proposed method using real images. The results show that the proposed system is a practical solution for motion estimation as well as for 3D reconstruction.