594 research outputs found
Keyframe-based visual–inertial odometry using nonlinear optimization
Combining visual and inertial measurements has become popular in mobile robotics, since the two sensing modalities offer complementary characteristics that make them the ideal choice for accurate visual–inertial odometry or simultaneous localization and mapping (SLAM). While historically the problem has been addressed with filtering, advancements in visual estimation suggest that nonlinear optimization offers superior accuracy, while still tractable in complexity thanks to the sparsity of the underlying problem. Taking inspiration from these findings, we formulate a rigorously probabilistic cost function that combines reprojection errors of landmarks and inertial terms. The problem is kept tractable and thus ensuring real-time operation by limiting the optimization to a bounded window of keyframes through marginalization. Keyframes may be spaced in time by arbitrary intervals, while still related by linearized inertial terms. We present evaluation results on complementary datasets recorded with our custom-built stereo visual–inertial hardware that accurately synchronizes accelerometer and gyroscope measurements with imagery. A comparison of both a stereo and monocular version of our algorithm with and without online extrinsics estimation is shown with respect to ground truth. Furthermore, we compare the performance to an implementation of a state-of-the-art stochastic cloning sliding-window filter. This competitive reference implementation performs tightly coupled filtering-based visual–inertial odometry. While our approach declaredly demands more computation, we show its superior performance in terms of accuracy
Ground-VIO: Monocular Visual-Inertial Odometry with Online Calibration of Camera-Ground Geometric Parameters
Monocular visual-inertial odometry (VIO) is a low-cost solution to provide
high-accuracy, low-drifting pose estimation. However, it has been meeting
challenges in vehicular scenarios due to limited dynamics and lack of stable
features. In this paper, we propose Ground-VIO, which utilizes ground features
and the specific camera-ground geometry to enhance monocular VIO performance in
realistic road environments. In the method, the camera-ground geometry is
modeled with vehicle-centered parameters and integrated into an
optimization-based VIO framework. These parameters could be calibrated online
and simultaneously improve the odometry accuracy by providing stable
scale-awareness. Besides, a specially designed visual front-end is developed to
stably extract and track ground features via the inverse perspective mapping
(IPM) technique. Both simulation tests and real-world experiments are conducted
to verify the effectiveness of the proposed method. The results show that our
implementation could dramatically improve monocular VIO accuracy in vehicular
scenarios, achieving comparable or even better performance than state-of-art
stereo VIO solutions. The system could also be used for the auto-calibration of
IPM which is widely used in vehicle perception. A toolkit for ground feature
processing, together with the experimental datasets, would be made open-source
(https://github.com/GREAT-WHU/gv_tools)
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