J
Joel A. Hesch
Researcher at Google
Publications - 49
Citations - 2280
Joel A. Hesch is an academic researcher from Google. The author has contributed to research in topics: Observability & Inertial navigation system. The author has an hindex of 22, co-authored 49 publications receiving 1950 citations. Previous affiliations of Joel A. Hesch include Microsoft & ETH Zurich.
Papers
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Proceedings ArticleDOI
A Direct Least-Squares (DLS) method for PnP
TL;DR: This work forms a nonlinear least-squares cost function whose optimality conditions constitute a system of three third-order polynomials, and employs the multiplication matrix to determine all the roots of the system analytically, and hence all minima of the LS, without requiring iterations or an initial guess of the parameters.
Proceedings ArticleDOI
Get Out of My Lab: Large-scale, Real-Time Visual-Inertial Localization
TL;DR: This paper demonstrates that large-scale, real-time pose estimation and tracking can be performed on mobile platforms with limited resources without the use of an external server by employing map and descriptor compression schemes as well as efficient search algorithms from computer vision.
Journal ArticleDOI
Camera-IMU-based localization: Observability analysis and consistency improvement
TL;DR: This work introduces a new methodology for determining the unobservable directions of nonlinear systems by factorizing the observability matrix according to the observable and unobserved modes, and applies this method to the VINS nonlinear model and determine its unobservables directions analytically.
Journal ArticleDOI
Consistency Analysis and Improvement of Vision-aided Inertial Navigation
TL;DR: An observability constrained VINS (OC-VINS), which explicitly enforces the unobservable directions of the system, hence preventing spurious information gain and reducing inconsistency is developed.
Book ChapterDOI
On the Consistency of Vision-Aided Inertial Navigation
TL;DR: This paper proposes an Observability-Constrained VINS (OC-VINS) methodology that explicitly adheres to the observability properties of the true system, and applies this approach to the Multi-State Constraint Kalman Filter (MSC-KF).