Structure from motion algorithms have an inherent limitation that the reconstruction can only be determined up to the unknown scale factor. Modern mobile devices are equipped with an inertial measurement unit (IMU), which can be used for estimating the scale of the reconstruction. We propose a method that recovers the metric scale given inertial measurements and camera poses. In the process, we also perform a temporal and spatial alignment of the camera and the IMU. Therefore, our solution can be easily combined with any existing visual reconstruction software. The method can cope with noisy camera pose estimates, typically caused by motion blur or rolling shutter artifacts, via utilizing a Rauch-Tung-Striebel (RTS) smoother. Furthermore, the scale estimation is performed in the frequency domain, which provides more robustness to inaccurate sensor time stamps and noisy IMU samples than the previously used time domain representation. In contrast to previous methods, our approach has no parameters that need to be tuned for achieving a good performance. In the experiments, we show that the algorithm outperforms the state-of-the-art in both accuracy and convergence speed of the scale estimate. The accuracy of the scale is around @math from the ground truth depending on the recording. We also demonstrate that our method can improve the scale accuracy of the Project Tango's build-in motion tracking.
The fusion of visual and inertial measurements has been a popular research topic in the robotics community. Most previous systems are focused on real-time tracking and navigation, e.g. @cite_20 @cite_28 @cite_12 @cite_30 @cite_24 @cite_1 @cite_25 @cite_9 @cite_6 @cite_10 . These approaches require tightly integrated sensor fusion, which places requirements for the hardware. For example, the synchronization of individual video frames and IMU sensor timestamps must be relatively accurate, and often the used IMUs are of notably better quality than standard smartphone IMUs, which are not aimed for inertial navigation purposes. In fact, many of the previous approaches utilize special hardware setups. For instance, both @cite_34 and @cite_17 use a similar synchronized IMU and stereo camera hardware prototype. Further, also @cite_15 @cite_13 @cite_18 @cite_11 use custom-made camera-IMU hardware. Finally, perhaps the most well-known example of a specialized hardware platform for visual-inertial odometry is the Google Tango tablet device which utilizes a fish-eye lens camera @cite_38 . Regarding Google Tango device, it should be noticed that the implementation is proprietary and not openly documented, and hence it is difficult to analyze whether similar performance could be realized with more conventional smartphone hardware.
@cite_30 @cite_13 @cite_18 @cite_11 @cite_38 @cite_28 @cite_9 @cite_1 @cite_6 @cite_34 @cite_24 @cite_15 @cite_20 @cite_10 @cite_25 @cite_12 @cite_17
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One camera and one low-cost inertial measurement unit (IMU) form a monocular visual-inertial system (VINS), which is the minimum sensor suite (in size, weight, and power) for the metric six degrees-of-freedom (DOF) state estimation. In this paper, we present VINS-Mono: a robust and versatile monocular visual-inertial state estimator. Our approach starts with a robust procedure for estimator initialization. A tightly coupled, nonlinear optimization-based method is used to obtain highly accurate visual-inertial odometry by fusing preintegrated IMU measurements and feature observations. A loop detection module, in combination with our tightly coupled formulation, enables relocalization with minimum computation. We additionally perform 4-DOF pose graph optimization to enforce the global consistency. Furthermore, the proposed system can reuse a map by saving and loading it in an efficient way. The current and previous maps can be merged together by the global pose graph optimization. We validate the performance of our system on public datasets and real-world experiments and compare against other state-of-the-art algorithms. We also perform an onboard closed-loop autonomous flight on the microaerial-vehicle platform and port the algorithm to an iOS-based demonstration. We highlight that the proposed work is a reliable, complete, and versatile system that is applicable for different applications that require high accuracy in localization. We open source our implementations for both PCs ( https: github.com HKUST-Aerial-Robotics VINS-Mono ) and iOS mobile devices ( https: github.com HKUST-Aerial-Robotics VINS-Mobile ). Vision-aided inertial navigation systems (V-INSs) can provide precise state estimates for the 3-D motion of a vehicle when no external references (e.g., GPS) are available. This is achieved by combining inertial measurements from an inertial measurement unit (IMU) with visual observations from a camera under the assumption that the rigid transformation between the two sensors is known. Errors in the IMU-camera extrinsic calibration process cause biases that reduce the estimation accuracy and can even lead to divergence of any estimator processing the measurements from both sensors. In this paper, we present an extended Kalman filter for precisely determining the unknown transformation between a camera and an IMU. Contrary to previous approaches, we explicitly account for the time correlation of the IMU measurements and provide a figure of merit (covariance) for the estimated transformation. The proposed method does not require any special hardware (such as spin table or 3-D laser scanner) except a calibration target. Furthermore, we employ the observability rank criterion based on Lie derivatives and prove that the nonlinear system describing the IMU-camera calibration process is observable. Simulation and experimental results are presented that validate the proposed method and quantify its accuracy. Visual and inertial sensors, in combination, are able to provide accurate motion estimates and are well suited for use in many robot navigation tasks. However, correct data fusion, and hence overall performance, depends on careful calibration of the rigid body transform between the sensors. Obtaining this calibration information is typically difficult and time-consuming, and normally requires additional equipment. In this paper we describe an algorithm, based on the unscented Kalman filter, for self-calibration of the transform between a camera and an inertial measurement unit (IMU). Our formulation rests on a differential geometric analysis of the observability of the cameraâIMU system; this analysis shows that the sensor-to-sensor transform, the IMU gyroscope and accelerometer biases, the local gravity vector, and the metric scene structure can be recovered from camera and IMU measurements alone. While calibrating the transform we simultaneously localize the IMU and build a map of the surroundings, all without additional hardware or prior knowledge about the environment in which a robot is operating. We present results from simulation studies and from experiments with a monocular camera and a low-cost IMU, which demonstrate accurate estimation of both the calibration parameters and the local scene structure. There have been increasing demands for developing microaerial vehicles with vision-based autonomy for search and rescue missions in complex environments. In particular, the monocular visual–inertial system (VINS), which consists of only an inertial measurement unit (IMU) and a camera, forms a great lightweight sensor suite due to its low weight and small footprint. In this paper, we address two challenges for rapid deployment of monocular VINS: 1) the initialization problem and 2) the calibration problem. We propose a methodology that is able to initialize velocity, gravity, visual scale, and camera–IMU extrinsic calibration on the fly. Our approach operates in natural environments and does not use any artificial markers. It also does not require any prior knowledge about the mechanical configuration of the system. It is a significant step toward plug-and-play and highly customizable visual navigation for mobile robots. We show through online experiments that our method leads to accurate calibration of camera–IMU transformation, with errors less than 0.02 m in translation and 1° in rotation. We compare out method with a state-of-the-art marker-based offline calibration method and show superior results. We also demonstrate the performance of the proposed approach in large-scale indoor and outdoor experiments.