NOCT: Nonlinear Observability with Constraints and Time Offset
Jianzhu Huai, Yukai Lin, Yujia Zhang

TL;DR
This paper introduces a method to analyze the observability of nonlinear affine control systems with constraints and time offsets, extending classical approaches and validating findings through visual inertial odometry applications.
Contribution
It presents a procedure to convert constrained nonlinear models into standard forms suitable for classic observability analysis, specifically addressing constraints and time offsets.
Findings
Unobservable variables under degenerate motion match previous linearized VIO results.
Extended observability analysis for time offset beyond prior studies.
Validated findings through simulation experiments.
Abstract
Nonlinear systems of affine control inputs overarch many sensor fusion instances. Analyzing whether a state variable in such a nonlinear system can be estimated (i.e., observability) informs better estimator design. Among the research on local observability of nonlinear systems, approaches based on differential geometry have attracted much attention for the solid theoretic foundation and suitability to automated deduction. Such approaches usually work with a system model of unconstrained control inputs and assume that the control inputs and observation outputs are timestamped by the same clock. To our knowledge, it has not been shown how to conduct the observability analysis with additional constraints enforced on the system's observations or control inputs. To this end, we propose procedures to convert a system model of affine control inputs with linear constraints into a…
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Taxonomy
TopicsAdvanced Vision and Imaging · Robotics and Sensor-Based Localization · Image and Object Detection Techniques
