15 accepted papers
Visual-inertial odometry (VIO), which fuses noisy inertial readings and camera measurements to provide 3D motion tracking, is a foundational component in many autonomous applications. With the increasing use of next-generation edge devices (e.g., AR/VR devices, nano drones, and mobile robotics) that
Energy-efficient visual-inertial motion tracking on SWAP-constrained edge devices (e.g., drones and AR glasses) is essential but challenging. Our previous work [1] introduced the first-of-its-kind quantized visual-inertial odometry (QVIO), utilizing either raw measurement quantization (zQVIO) or sin
The system observability analysis is of practical importance, for example, due to its ability to identify the unobservable directions of the estimated state which can influence estimation accuracy and help develop consistent and robust estimators. Recent studies focused on analyzing the observabilit…
In this paper, we introduce a novel and efficient technique for consistent covariance recovery in nonlinear optimization-based Visual-Inertial Navigation Systems (VINS). Estimating uncertainty in real-time is crucial for evaluating system performance and enhancing downstream operations such as data…
Achieving efficient and consistent localization with a prior map remains challenging in robotics. Conventional keyframe-based approaches often suffer from sub-optimal viewpoints due to limited field of view (FOV) and/or constrained motion, thus degrading the localization performance. To address this…
As edge devices equipped with cameras and inertial measurement units (IMUs) are emerging, it holds huge implications to endow these mobile devices with spatial computing capability. However, ultra-efficient visual-inertial estimation at the size, weight and power (SWAP)-constrained edge devices to p…
In this paper, we strongly advocate square-root covariance (instead of information) filtering for Visual-Inertial Navigation Systems (VINS), in particular on resource-constrained edge devices, because of its superior efficiency and numerical stability. Although VINS have made tremendous progress in…
In monocular visual-inertial navigation systems, it is ideal to initialize as quickly and robustly as possible. State-of-the- art initialization methods typically make linear approximations using the image features and inertial information in order to initialize in closed-form, and then refine t…
State-of-the-art monocular visual-inertial odometry (VIO) approaches rely on sparse point features in part due to their efficiency, robustness, and prevalence, while ignoring high-level structural regularities such as planes that are common to man-made environments and can be exploited to further co…
In this work, we present a comprehensive analysis of the application of the First-estimates Jacobian (FEJ) design methodology in nonlinear optimization-based Visual-Inertial Navigation Systems (VINS). The FEJ approach fixes system linearization points to preserve proper observability properties of V…
The invariant extended Kalman filter (IEKF) is proven to preserve the observability property of visual-inertial navigation systems (VINS) and suitable for consistent estimator design. However, if features are maintained in the state vector, the propagation of IEKF will become more computationally ex
In this paper, we propose a novel consistent state estimator design for visual-inertial systems. Motivated by first-estimates Jacobian (FEJ) based estimators - which uses the first-ever estimates as linearization points to preserve proper observability properties of the linearized estimator thereby…
System modeling and parameter identification of micro aerial vehicles (MAV) are crucial for robust autonomy, especially under highly dynamic motions. Visual-inertial-aided online parameter identification has recently seen research attention due to the demanding of adaptation to platform configuratio…
Batch optimization based inertial measurement unit (IMU) and visual sensor fusion enables high rate localization for many robotic tasks. However, it remains a challenge to ensure that the batch optimization is computationally efficient while being consistent for high rate IMU measurements without ma…
Aiming for a lightweight and robust localization solution for low-cost, low-power autonomous robot platforms, such as educational or industrial ground vehicles, under challenging conditions (e.g., poor sensor calibration, low lighting and dynamic objects), we propose a two-stage localization system…