Omnidirectional Stereo Vision for Autonomous Vehicles

Environment perception with cameras is an important requirement for many applications for autonomous vehicles and robots. This work presents a stereoscopic omnidirectional camera system for autonomous vehicles which resolves the problem of a limited field of view and provides a 360° panoramic view of the environment. We present a new projection model for these cameras and show that the camera setup overcomes major drawbacks of traditional perspective cameras in many applications

The Geometry and Usage of the Supplementary Fisheye Lenses in Smartphones

Nowadays, mobile phones are more than a device that can only satisfy the communication need between people. Since fisheye lenses integrated with mobile phones are lightweight and easy to use, they are advantageous. In addition to this advantage, it is experimented whether fisheye lens and mobile phone combination can be used in a photogrammetric way, and if so, what will be the result. Fisheye lens equipment used with mobile phones was tested in this study. For this, standard calibration of ‘Olloclip 3 in one’ fisheye lens used with iPhone 4S mobile phone and ‘Nikon FC‐E9’ fisheye lens used with Nikon Coolpix8700 are compared based on equidistant model. This experimental study shows that Olloclip 3 in one fisheye lens developed for mobile phones has at least the similar characteristics with classic fisheye lenses. The dimensions of fisheye lenses used with smart phones are getting smaller and the prices are reducing. Moreover, as verified in this study, the accuracy of fisheye lenses used in smartphones is better than conventional fisheye lenses. The use of smartphones with fisheye lenses will give the possibility of practical applications to ordinary users in the near future

Exploiting line metric reconstruction from non-central circular panoramas

In certain non-central imaging systems, straight lines are projected via a non-planar surface encapsulating the 4 degrees of freedom of the 3D line. Consequently the geometry of the 3D line can be recovered from a minimum of four image points. However, with classical non-central catadioptric systems there is not enough effective baseline for a practical implementation of the method. In this paper we propose a multi-camera system configuration resembling the circular panoramic model which results in a particular non-central projection allowing the stitching of a non-central panorama. From a single panorama we obtain well-conditioned 3D reconstruction of lines, which are specially interesting in texture-less scenarios. No previous information about the direction or arrangement of the lines in the scene is assumed. The proposed method is evaluated on both synthetic and real images

Efficient generic calibration method for general cameras with single centre of projection

Generic camera calibration is a non-parametric calibration technique that is applicable to any type of vision sensor. However, the standard generic calibration method was developed with the goal of generality and it is therefore sub-optimal for the common case of cameras with a single centre of projection (e.g. pinhole, fisheye, hyperboloidal catadioptric). This paper proposes novel improvements to the standard generic calibration method for central cameras that reduce its complexity, and improve its accuracy and robustness. Improvements are achieved by taking advantage of the geometric constraints resulting from a single centre of projection. Input data for the algorithm is acquired using active grids, the performance of which is characterised. A new linear estimation stage to the generic algorithm is proposed incorporating classical pinhole calibration techniques, and it is shown to be significantly more accurate than the linear estimation stage of the standard method. A linear method for pose estimation is also proposed and evaluated against the existing polynomial method. Distortion correction and motion reconstruction experiments are conducted with real data for a hyperboloidal catadioptric sensor for both the standard and proposed methods. Results show the accuracy and robustness of the proposed method to be superior to those of the standard method

Enhancing 3D Visual Odometry with Single-Camera Stereo Omnidirectional Systems

We explore low-cost solutions for efficiently improving the 3D pose estimation problem of a single camera moving in an unfamiliar environment. The visual odometry (VO) task -- as it is called when using computer vision to estimate egomotion -- is of particular interest to mobile robots as well as humans with visual impairments. The payload capacity of small robots like micro-aerial vehicles (drones) requires the use of portable perception equipment, which is constrained by size, weight, energy consumption, and processing power. Using a single camera as the passive sensor for the VO task satisfies these requirements, and it motivates the proposed solutions presented in this thesis. To deliver the portability goal with a single off-the-shelf camera, we have taken two approaches: The first one, and the most extensively studied here, revolves around an unorthodox camera-mirrors configuration (catadioptrics) achieving a stereo omnidirectional system (SOS). The second approach relies on expanding the visual features from the scene into higher dimensionalities to track the pose of a conventional camera in a photogrammetric fashion. The first goal has many interdependent challenges, which we address as part of this thesis: SOS design, projection model, adequate calibration procedure, and application to VO. We show several practical advantages for the single-camera SOS due to its complete 360-degree stereo views, that other conventional 3D sensors lack due to their limited field of view. Since our omnidirectional stereo (omnistereo) views are captured by a single camera, a truly instantaneous pair of panoramic images is possible for 3D perception tasks. Finally, we address the VO problem as a direct multichannel tracking approach, which increases the pose estimation accuracy of the baseline method (i.e., using only grayscale or color information) under the photometric error minimization as the heart of the “direct” tracking algorithm. Currently, this solution has been tested on standard monocular cameras, but it could also be applied to an SOS. We believe the challenges that we attempted to solve have not been considered previously with the level of detail needed for successfully performing VO with a single camera as the ultimate goal in both real-life and simulated scenes

A visual servoing path-planning strategy for cameras obeying the unified model

Part of 2010 IEEE Multi-Conference on Systems and ControlRecently, a unified camera model has been introduced in visual control systems in order to describe through a unique mathematical model conventional perspective cameras, fisheye cameras, and catadioptric systems. In this paper, a path-planning strategy for visual servoing is proposed for any camera obeying this unified model. The proposed strategy is based on the projection onto a virtual plane of the available image projections. This has two benefits. First, it allows one to perform camera pose estimation and 3D object reconstruction by using methods for conventional camera that are not valid for other cameras. Second, it allows one to perform image pathplanning for multi-constraint satisfaction by using a simplified but equivalent projection model, that in this paper is addressed by introducing polynomial parametrizations of the rotation and translation. The planned image trajectory is hence tracked by using an IBVS controller. The proposed strategy is validated through simulations with image noise and calibration errors typical of real experiments. It is worth remarking that visual servoing path-planning for non conventional perspective cameras has not been proposed yet in the literature. © 2010 IEEE.published_or_final_versionThe 2010 IEEE International Symposium on Computer-Aided Control System Design (CACSD), Yokohama, Japan, 8-10 September 2010. In Proceedings of CACSD, 2010, p. 1795-180

A Lightweight and Cost-Effective 3D Omnidirectional Depth Sensor Based on Laser Triangulation

In this paper, we propose a new lightweight and cost-effective 3D omnidirectional depth sensor based on laser triangulation in order to ensure a wide field of view (FOV) while achieving portability and affordability. The proposed sensor is tiny palm-sized and hence easily installed even on small moving objects, which is largely composed of a structured light-based 2D sensor and a rotating motor for creating a full 360 degree horizontal FOV, thus providing a 3D omnidirectional sensing capability. The structured light-based 2D sensor is specially designed to maximize the vertical FOV by employing a fisheye camera and a laser beam passing through two cylindrical lenses for projecting a line onto a surface. From the rotational movement of the 2D sensor due to the mounted motor, its surroundings are scanned by extracting the corresponding 3D omnidirectional depth information from laser triangulation. The actual implementation is carried out to examine the technical feasibility of realizing the proposed 3D omnidirectioanl depth sensor. It turns out that the proposed depth sensor covers over 97% area of its surrounding sphere. It is also observed through experiments that the proposed 3D omnidirectional depth sensor has similar accuracy to that of a Velodyne HDL-32, 32-channel light detection and ranging (LIDAR) sensor, at a range of 5 m to 6 m while providing much wider vertical FOV and higher vertical resolution.11Ysciescopu

OMNIDIRECTIONAL IMAGE PROCESSING USING GEODESIC METRIC

International audienceDue to distorsions of catadioptric sensors, omnidirectional images can not be treated as classical images. If the equivalence between central catadioptric images and spherical images is now well known and used, spherical analysis often leads to complex methods particularly tricky to employ. In this paper, we propose to derive omnidirectional image treatments by using geodesic metric. We demonstrate that this approach allows to adapt efficiently classical image processing to omnidirectional images