Consistent Map-based 3D Localization on Mobile Devices
| Autor: | Joel A. Hesch, Ryan C. DuToit, Esha D. Nerurkar, Stergios I. Roumeliotis |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
FOS: Computer and information sciences
Hessian matrix 0209 industrial biotechnology Noise measurement Computer science MathematicsofComputing_NUMERICALANALYSIS 02 engineering and technology Filter (signal processing) Covariance symbols.namesake Computer Science - Robotics 020901 industrial engineering & automation Distribution (mathematics) Quadratic equation 0202 electrical engineering electronic engineering information engineering symbols Measurement uncertainty 020201 artificial intelligence & image processing Robotics (cs.RO) Algorithm Cholesky decomposition |
| Zdroj: | ICRA |
| Popis: | In this paper, we seek to provide consistent, real-time 3D localization capabilities to mobile devices navigating within previously mapped areas. To this end, we introduce the Cholesky-Schmidt-Kalman filter (C-SKF), which explicitly considers the uncertainty of the prior map, by employing the sparse Cholesky factor of the map's Hessian, instead of its dense covariance-as is the case for the Schmidt-Kalman filter. By doing so, the C-SKF has memory requirements typically linear in the size of the map, as opposed to quadratic for storing the map's covariance. Moreover, and in order to bound the processing needs of the C-SKF (between linear and quadratic in the size of the map), we introduce two relaxations of the C-SKF algorithm: (i) The sC-SKF, which operates on the Cholesky factors of independent sub-maps resulting from dividing the map into overlapping segments. (ii) We formulate an efficient method for sparsifying the Cholesky factor by selecting and processing a subset of loop-closure measurements based on their temporal distribution. Lastly, we assess the processing and memory requirements of the proposed algorithms, and compare their positioning accuracy against other inconsistent map-based localization approaches that employ measurement-noise-covariance inflation to compensate for the map's uncertainty. |
| Databáze: | OpenAIRE |
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