English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

As cameras and inertial sensors are becoming ubiquitous in mobile devices androbots, it holds great potential to design visual-inertial navigation systems(VINS) for efficient versatile 3D motion tracking which utilize any (multiple)available cameras and inertial measurement units (IMUs) and are resilient tosensor failures or measurement depletion. To this end, rather than the standardVINS paradigm using a minimal sensing suite of a single camera and IMU, in thispaper we design a real-time consistent multi-IMU multi-camera (MIMC)-VINSestimator that is able to seamlessly fuse multi-modal information from anarbitrary number of uncalibrated cameras and IMUs. Within an efficientmulti-state constraint Kalman filter (MSCKF) framework, the proposed MIMC-VINSalgorithm optimally fuses asynchronous measurements from all sensors, whileproviding smooth, uninterrupted, and accurate 3D motion tracking even if somesensors fail. The key idea of the proposed MIMC-VINS is to perform high-orderon-manifold state interpolation to efficiently process all available visualmeasurements without increasing the computational burden due to estimatingadditional sensors' poses at asynchronous imaging times. In order to fuse theinformation from multiple IMUs, we propagate a joint system consisting of allIMU states while enforcing rigid-body constraints between the IMUs during thefilter update stage. Lastly, we estimate online both spatiotemporal extrinsicand visual intrinsic parameters to make our system robust to errors in priorsensor calibration. The proposed system is extensively validated in bothMonte-Carlo simulations and real-world experiments.

MIMC-VINS: A Versatile and Resilient Multi-IMU Multi-Camera Visual-Inertial Navigation System