Optimization-Based Online Initialization and Calibration of Monocular Visual-Inertial Odometry Considering Spatial-Temporal Constraints

Abstract

The online system state initialization and simultaneous spatial-temporal calibration are critical for monocular Visual-Inertial Odometry (VIO) since these parameters are either not well provided or even unknown. Although impressive performance has been achieved, most of the existing methods are designed for filter-based VIOs. For the optimization-based VIOs, there is not much online spatial-temporal calibration method in the literature. In this paper, we propose an optimization-based online initialization and spatial-temporal calibration method for VIO. The method does not need any prior knowledge about spatial and temporal configurations. It estimates the initial states of metric-scale, velocity, gravity, Inertial Measurement Unit (IMU) biases, and calibrates the coordinate transformation and time offsets between the camera and IMU sensors. The work routine of the method is as follows. First, it uses a time offset model and two short-term motion interpolation algorithms to align and interpolate the camera and IMU measurement data. Then, the aligned and interpolated results are sent to an incremental estimator to estimate the initial states and the spatial–temporal parameters. After that, a bundle adjustment is additionally included to improve the accuracy of the estimated results. Experiments using both synthetic and public datasets are performed to examine the performance of the proposed method. The results show that both the initial states and the spatial-temporal parameters can be well estimated. The method outperforms other contemporary methods used for comparison.

Enhanced EKF-Based Time Calibration for GNSS/UWB Tight Integration

“…Conversely, in case the first-order derivative of f (j) (t d,k ) approached a null value for some interval in the support, t d,k estimation would be jeopardised. By differentiating (28), it is obtained: which highlights that, the smaller is the inner product between the receiver velocity vector v u,k and the steering vector to the j-th UWB anchor location h (j) U,k , the weaker is the local identifiability characterising t d,k . This is equivalent to say that the auxiliary UWB ranging observable associated to the j-th UWB anchor brings little, if any, information to the integration filter to support accurate t d,k estimation.…”

“…Moreover, [26], [27] perform visual-inertial time calibration by temporally aligning orientation curves sensed by the independent sensors. Yet joint multisensor optimisationbased calibration strategies are explored in [28], [29]. As opposed to offline techniques, online temporal calibration via filtering-based methods (i.e., filtering-based calibration) is a promising alternative [30], [31]; this strategy models and recursively estimates the time-offset as an additional state-vector unknown under the hybridisation filter state-space formulation [31], [32].…”

Enhanced EKF-based Time Calibration for GNSS/UWB Tight Integration

“…Leveraging the discrete-time process model (25) to evaluate ϵ r,k for dynamic compensation, ( 17) can be rewritten as:…”

“…Moreover, [23], [24] have formulated visual-inertial time calibration as a registration task by temporally aligning orientation curves sensed by the independent sensors. Yet joint multisensor optimisationbased calibration strategies have been proposed in [25], [26]. Alternatively, the integration of time calibration into a stateestimation framework is an established approach which involves the modelling of the unknown time-offset as part of the hybridisation filter state-space formulation [27]- [29].…”

MAV Localization in Large-Scale Environments: A Decoupled Optimization/Filtering Approach

“…Visual-inertial odometry (VIO) is one of the most mature and well-established approaches in the localization field [ 2 , 3 , 4 ]. Efficient visual odometry can be achieved using a high-quality perception of the surroundings.…”