11 accepted papers
Large language models (LLMs) frequently refuse to respond to pseudo-malicious instructions: semantically harmless input queries triggering unnecessary LLM refusals due to conservative safety alignment, significantly impairing user experience. Collecting such instructions is crucial for evaluating an…
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
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…
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…
We formulate the first differentiable analog quantum computing framework with specific parameterization design at the analog signal (pulse) level to better exploit near-term quantum devices via variational methods. We further propose a scalable approach to estimate the gradients of quantum dynamics…
The topic of multi-person pose estimation has beenlargely improved recently, especially with the developmentof convolutional neural network. However, there still exista lot of challenging cases, such as occluded keypoints, in-visible keypoints and complex background, which cannot bewell addr…