In this paper we propose a robust visual odometry system for a wide-baseline camera rig with wide field-of-view (FOV) fisheye lenses, which provides full omnidirectional stereo observations of the environment. For more robust and accurate ego-motion estimation we adds three components to the standard VO pipeline, 1) the hybrid projection model for improved feature matching, 2) multi-view P3P RANSAC algorithm for pose estimation, and 3) online update of rig extrinsic parameters. The hybrid projection model combines the perspective and cylindrical projection to maximize the overlap between views and minimize the image distortion that degrades feature matching performance. The multi-view P3P RANSAC algorithm extends the conventional P3P RANSAC to multi-view images so that all feature matches in all views are considered in the inlier counting for robust pose estimation. Finally the online extrinsic calibration is seamlessly integrated in the backend optimization framework so that the changes in camera poses due to shocks or vibrations can be corrected automatically. The proposed system is extensively evaluated with synthetic datasets with ground-truth and real sequences of highly dynamic environment, and its superior performance is demonstrated.
For improved environmental awareness and perception capabilities, multi-camera methods also have been studied. @cite_18 present a visual odometry algorithm for a multi-camera system which can observe full surrounding view. They successfully estimate ego-motion with the 2-point algorithm showing the importance of the inter-camera correspondences to recover metric scale. @cite_3 implement a visual SLAM and self-calibration system with at least one calibrated-stereo camera and an arbitrary number of monocular cameras where they have overlapping views with the stereo camera. Recently, a robust multi-camera system using direct methods with plane sweeping stereo is proposed by @cite_20 . Finding correspondences between fisheye images is a challenging and important problem and many researchers devoted efforts in it. Special descriptors @cite_7 @cite_8 are designed to consider the distortion, and @cite_12 and @cite_15 proposed dense matching algorithms for fisheye images.
@cite_18 @cite_7 @cite_8 @cite_3 @cite_15 @cite_12 @cite_20
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In this paper, we present a visual ego-motion estimation algorithm for a self-driving car equipped with a close-to-market multi-camera system. By modeling the multi-camera system as a generalized camera and applying the non-holonomic motion constraint of a car, we show that this leads to a novel 2-point minimal solution for the generalized essential matrix where the full relative motion including metric scale can be obtained. We provide the analytical solutions for the general case with at least one inter-camera correspondence and a special case with only intra-camera correspondences. We show that up to a maximum of 6 solutions exist for both cases. We identify the existence of degeneracy when the car undergoes straight motion in the special case with only intra-camera correspondences where the scale becomes unobservable and provide a practical alternative solution. Our formulation can be efficiently implemented within RANSAC for robust estimation. We verify the validity of our assumptions on the motion model by comparing our results on a large real-world dataset collected by a car equipped with 4 cameras with minimal overlapping field-of-views against the GPS INS ground truth. In this paper, we develop an interest point detector and binary feature descriptor for spherical images. We take as inspiration a recent framework developed for planar images, BRISK (Binary Robust Invariant Scalable Keypoints), and adapt the method to operate on spherical images. All of our processing is intrinsic to the sphere and avoids the distortion inherent in storing and indexing spherical images in a 2D representation. We discretise images on a spherical geodesic grid formed by recursive subdivision of a triangular mesh. This leads to a multiscale pixel grid comprising mainly hexagonal pixels that lends itself naturally to a spherical image pyramid representation. For interest point detection, we use a variant of the Accelerated Segment Test (AST) corner detector which operates on our geodesic grid. We estimate a continuous scale and location for features and descriptors are built by sampling onto a regular pattern in the tangent space. We evaluate repeatability, precision and recall on both synthetic spherical images with known ground truth correspondences and real images. In this paper, we propose SPHORB, a new fast and robust binary feature detector and descriptor for spherical panoramic images. In contrast to state-of-the-art spherical features, our approach stems from the geodesic grid, a nearly equal-area hexagonal grid parametrization of the sphere used in climate modeling. It enables us to directly build fine-grained pyramids and construct robust features on the hexagonal spherical grid, thus avoiding the costly computation of spherical harmonics and their associated bandwidth limitation. We further study how to achieve scale and rotation invariance for the proposed SPHORB feature. Extensive experiments show that SPHORB consistently outperforms other existing spherical features in accuracy, efficiency and robustness to camera movements. The superior performance of SPHORB has also been validated by real-world matching tests. The use of a multi-camera system enables a robot to obtain a surround view, and thus, maximize its perceptual awareness of its environment. If vision-based simultaneous localization and mapping (vSLAM) is expected to provide reliable pose estimates for a micro aerial vehicle (MAV) with a multi-camera system, an accurate calibration of the multi-camera system is a necessary prerequisite. We propose a novel vSLAM-based self-calibration method for a multi-camera system that includes at least one calibrated stereo camera, and an arbitrary number of monocular cameras. We assume overlapping fields of view to only exist within stereo cameras. Our self-calibration estimates the inter-camera transforms with metric scale; metric scale is inferred from calibrated stereo. On our MAV, we set up each camera pair in a stereo configuration which facilitates the estimation of the MAV's pose with metric scale. Once the MAV is calibrated, the MAV is able to estimate its global pose via a multi-camera vSLAM implementation based on the generalized camera model. We propose a novel minimal and linear 3-point algorithm that uses relative rotation angle measurements from a 3-axis gyroscope to recover the relative motion of the MAV with metric scale and from 2D-2D feature correspondences. This relative motion estimation does not involve scene point triangulation. Our constant-time vSLAM implementation with loop closures runs on-board the MAV in real-time. To the best of our knowledge, no published work has demonstrated real-time on-board vSLAM with loop closures. We show experimental results from simulation experiments, and real-world experiments in both indoor and outdoor environments. We propose a novel omnidirectional stereo camera setup that is formed by two ultra-wide field-of-view (FOV) fisheye cameras. The proposed configuration is formed by two 245-degree FOV fisheye cameras, facing opposite directions, that are rigidly mounted on two sides of a rod. The overlapping view in the two fisheye images forms a ring-shaped spatial stereo setup. Our system provides stereo observations with full 360-degree FOV in horizontal directions and 65-degree FOV in the vertical direction. In addition, the two fisheye cameras altogether also provide full spherical monocular coverage of the surrounding environment. We address challenges in camera modeling, fisheye intrinsic calibration, stereo self-calibration, and depth estimation. We develop a lens-specific fisheye camera calibration method that uses manufacturer data for the optical lens to assist with the intrinsic calibration and develop an online self-calibration approach for estimating stereo extrinsic parameters. The overlapping camera views are rectified into stereo image pairs, from which a spatial stereo matching pipeline is developed for depth estimation in all horizontal directions. We show both qualitative and quantitative analysis to validate our approach. In this paper, we propose an adaptation of camera projection models for fisheye cameras into the plane-sweeping stereo matching algorithm. This adaptation allows us to do plane-sweeping stereo directly on fisheye images. Our approach also works for other non-pinhole cameras such as omni directional and catadioptric cameras when using the unified projection model. Despite the simplicity of our proposed approach, we are able to obtain full, good quality and high resolution depth maps from the fisheye images. To verify our approach, we show experimental results based on depth maps generated by our approach, and dense models produced from these depth maps. We present a visual odometry (VO) algorithm for a multi-camera system and robust operation in challenging environments. Our algorithm consists of a pose tracker and a local mapper. The tracker estimates the current pose by minimizing photometric errors between the most recent keyframe and the current frame. The mapper initializes the depths of all sampled feature points using plane-sweeping stereo. To reduce pose drift, a sliding window optimizer is used to refine poses and structure jointly. Our formulation is flexible enough to support an arbitrary number of stereo cameras. We evaluate our algorithm thoroughly on five datasets. The datasets were captured in different conditions: daytime, night-time with near-infrared (NIR) illumination and nighttime without NIR illumination. Experimental results show that a multi-camera setup makes the VO more robust to challenging environments, especially night-time conditions, in which a single stereo configuration fails easily due to the lack of features.