Pedro Miraldo

Competitions for Benchmarking: Task and Functionality Scoring Complete Performance Assessment

Scientific experiments and robotic competitions share some common traits that can put the debate about developing better experimental methodologies and replicability of results in robotics research on more solid ground. In this context, the Robot Competitions Kick Innovation in Cognitive Systems and Robotics (RoCKIn) project aims to develop competitions that come close to scientific experiments, providing an objective performance evaluation of robot systems under controlled and replicable conditions. In this article, by further articulating replicability into reproducibility and repeatability and by considering some results from the 2014 first RoCKIn competition, we show that the RoCKIn approach offers tools that enable the replicability of experimental results.

Calibration of Smooth Camera Models

Generic imaging models can be used to represent any camera. Current generic models are discrete and define a mapping between each pixel in the image and a straight line in 3D space. This paper presents a modification of the generic camera model that allows the simplification of the calibration procedure. The only requirement is that the coordinates of the 3D projecting lines are related by functions that vary smoothly across space. Such a model is obtained by modifying the general imaging model using radial basis functions (RBFs) to interpolate image coordinates and 3D lines, thereby allowing both an increase in resolution (due to their continuous nature) and a more compact representation. Using this variation of the general imaging model, we also develop a calibration procedure. This procedure only requires that a 3D point be matched to each pixel. In addition, not all the pixels need to be calibrated. As a result, the complexity of the procedure is significantly decreased. Normalization is applied to the coordinates of both image and 3D points, which increases the accuracy of the calibration. Results with both synthetic and real datasets show that the model and calibration procedure are easily applicable and provide accurate calibration results.

Pose Estimation for General Cameras Using Lines

In this paper, we address the problem of pose estimation under the framework of generalized camera models. We propose a solution based on the knowledge of the coordinates of 3-D straight lines (expressed in the world coordinate frame) and their corresponding image pixels. Previous approaches used the knowledge of the coordinates of 3-D points (zero dimensional elements) and their corresponding images (zero dimensional elements). In this paper, pixels belonging to the image of 3-D lines are used. There is no need to establish correspondences between pixels and 3-D points. Correspondences are established between 3-D lines and their images. There is no need to identify individual pixels. The use of correspondences between pixels (that belong to the images of the 3-D lines) and 3-D lines facilitates the correspondence problem when compared to the use of world and image points. This is one of the contributions of this paper. The approach is both evaluated and validated using synthetic data and also real images.

Planar pose estimation for general cameras using known 3D lines

In this article, we address the pose estimation for planar motion under the framework of generalized camera models. We assume the knowledge of the coordinates of 3D straight lines in the world coordinate system. Pose is estimated using the images of the 3D lines. This approach does not require the determination of correspondences between pixels and 3D world points. Instead, and for each pixel, it is only required that we determine to which 3D line it is associated with. Instead of identifying individual pixels, it is only necessary to establish correspondences between the pixels that belong to the images of the 3D lines, and the 3D lines. Moreover and using the assumption that the motion is planar, this paper presents a novel method for the computation of the pose using general imaging devices and assuming the knowledge of the coordinates of 3D straight lines. The approach is evaluated and validated using both synthetic data and real images. The experiments are performed using a mobile robot equipped with a non-central camera.

Point-based calibration using a parametric representation of the general imaging model

Generic imaging models can be used to represent any camera. These models are specially suited for non-central cameras for which closed-form models do not exist. Current models are discrete and define a mapping between each pixel in the image and a straight line in 3D space. Due to difficulties in the calibration procedure and model complexity these methods have not been used in practice. The focus of our work was to relax these drawbacks. In this paper we modify the general imaging model using radial basis functions to interpolate image coordinates and 3D lines allowing both an increase in resolution (due to their continuous nature) and a more compact representation. Using this new variation of the general imaging model we also develop a new linear calibration procedure. In this process it is only required to match one 3D point to each image pixel. Also it is not required the calibration of every image pixel. As a result the complexity of the procedure is significantly decreased.

A real-time Deep Learning pedestrian detector for robot navigation

A real-time Deep Learning based method for Pedestrian Detection (PD) is applied to the Human-Aware robot navigation problem. The pedestrian detector combines the Aggregate Channel Features (ACF) detector with a deep Convolutional Neural Network (CNN) in order to obtain fast and accurate performance. Our solution is firstly evaluated using a set of real images taken from onboard and offboard cameras and, then, it is validated in a typical robot navigation environment with pedestrians (two distinct experiments are conducted). The results on both tests show that our pedestrian detector is robust and fast enough to be used on robot navigation applications.

A simple and robust solution to the minimal general pose estimation

In this article we address the problem of minimal pose under the framework of the generalized camera models. Previous approaches were based on geometric properties, such as the preservation of distance between points. In this paper we propose a novel formulation of the problem using an algebraic-based approach. We represent the pose by a 3×3 matrix. Using both the algebraic relationship between three incident 3D points and straight lines and the underlying constraints of the pose matrix, pose can be computed. In terms of experimental results, the main contribution of the proposed method is the robustness to critical configurations. In addition, a full comparison and analysis between state-of-the-art methods is made (so far, there is no published comparison between state-of-the-art methods published).

Pose estimation for non-central cameras using planes

In this paper we study pose estimation for non-central cameras, using planes. The method proposed uses non-minimal data. Using the homography matrix to represent the transformation between the world and camera coordinate systems, we describe a non-iterative algorithm for pose estimation. In addition, we propose a parameter optimization to refine the pose estimate. We evaluate the proposed solutions against the state-of-the-art method in terms of both robustness to noise and computation time. From the experiments, we conclude that the proposed method is more accurate against noise. We also conclude that the numerical results obtained with this method improve with increasing number of data points. In terms of processing speed both versions of the algorithm presented are faster than the state-of-the-art algorithm.

Direct Solution to the Minimal Generalized Pose

Pose estimation is a relevant problem for imaging systems whose applications range from augmented reality to robotics. In this paper we propose a novel solution for the minimal pose problem, within the framework of generalized camera models and using a planar homography. Within this framework and considering only the geometric elements of the generalized camera models, an imaging system can be modeled by a set of mappings associating image pixels to 3-D straight lines. This mapping is defined in a 3-D world coordinate system. Pose estimation performs the computation of the rigid transformation between the original 3-D world coordinate system and the one in which the camera was calibrated. Using synthetic data, we compare the proposed minimal-based method with the state-of-the-art methods in terms of numerical errors, number of solutions and processing time. From the experiments, we conclude that the proposed method performs better, especially because there is a smaller variation in numerical errors, while results are similar in terms of number of solutions and computation time. To further evaluate the proposed approach we tested our method with real data. One of the relevant contributions of this paper is theoretical. When compared to the state-of-the-art approaches, we propose a completely new parametrization of the problem that can be solved in four simple steps. In addition, our approach does not require any predefined transformation of the dataset, which yields a simpler solution for the problem.

Feature Based Potential Field for Low-Level Active Visual Navigation

This paper proposes a novel solution for improving visual localization in an active fashion. The solution, based on artificial potential field, associates each feature in the current image frame with an attractive or neutral potential energy. The resultant action drives the vehicle towards the goal, while still favoring feature rich areas. Experimental results with a mini quadrotor equipped with a downward looking camera assess the performance of the proposed method.