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Self-Calibration and Visual SLAM with a Multi-Camera System on a Micro Aerial Vehicle
"... Abstract—The use of a multi-camera system enables a robot to obtain a surround view, and thus, maximize its perceptual awareness of its environment. An accurate calibration is a nec-essary prerequisite if vision-based simultaneous localization and mapping (vSLAM) is expected to provide reliable pose ..."
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Abstract—The use of a multi-camera system enables a robot to obtain a surround view, and thus, maximize its perceptual awareness of its environment. An accurate calibration is a nec-essary prerequisite 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. On our MAV, we set up each camera pair in a stereo configuration. We propose a novel vSLAM-based self-calibration method for a multi-sensor system that includes multiple calibrated stereo cameras and an inertial measurement unit (IMU). Our self-calibration estimates the transform with metric scale between each camera and the IMU. 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 inertial information to recover the relative motion of the MAV with metric scale. 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 in both indoor and outdoor environments. The code for both the self-calibration and vSLAM is available as a set of ROS packages at
LÉBRALY et al.: CALIBRATION OF NON-OVERLAPPING CAMERAS 1 Calibration of Non-Overlapping Cameras- Application to Vision-Based Robotics
"... Multi-camera systems are more and more used in vision-based robotics. An accurate extrinsic calibration is usually required. In most of cases, this task is done by matching features through different views of the same scene. However, if the cameras fields of view do not overlap, such a matching proc ..."
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Multi-camera systems are more and more used in vision-based robotics. An accurate extrinsic calibration is usually required. In most of cases, this task is done by matching features through different views of the same scene. However, if the cameras fields of view do not overlap, such a matching procedure is not feasible anymore. This article deals with a simple and flexible extrinsic calibration method, for nonoverlapping camera rig. The aim is the calibration of non-overlapping cameras embedded on a vehicle, for visual navigation purpose in urban environment. The cameras do not see the same area at the same time. The calibration procedure consists in manoeuvring the vehicle while each camera observes a static scene. The main contributions are a study of the singular motions and a specific bundle adjustment which both reconstructs the scene and calibrates the cameras. Solutions to handle the singular configurations, such as planar motions, are exposed. The proposed approach has been validated with synthetic and real data. 1
Autonomous Robots manuscript No. (will be inserted by the editor) Self-Calibration and Visual SLAM with a Multi-Camera System on a Micro Aerial Vehicle
"... Abstract 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 mu ..."
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Abstract 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 trans-forms with metric scale; metric scale is inferred from cal-ibrated 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 cal-ibrated, the MAV is able to estimate its global pose via a multi-camera vSLAM implementation based on the general-ized camera model. We propose a novel minimal and linear 3-point algorithm that uses relative rotation angle measure-ments from a 3-axis gyroscope to recover the relative mo-tion of the MAV with metric scale from 2D-2D feature cor-respondences. This relative motion estimation does not in-volve scene point triangulation. Our constant-time vSLAM