Factor Graph-Based Smoothing Without Matrix Inversion for Highly Precise Localization
| Autor: | Paul Chauchat, Axel Barrau, Silvere Bonnabel |
|---|---|
| Přispěvatelé: | Institut Supérieur de l'Aéronautique et de l'Espace (ISAE-SUPAERO), Safran Tech, SAFRAN Group, Institut de sciences exactes et appliquées (ISEA), Université de la Nouvelle-Calédonie (UNC), Centre de Robotique (CAOR), MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL) |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Optimization 0209 industrial biotechnology Optimization problem Computer science Covariance matrices Factor graphs Autonomous vehicles Systems and Control (eess.SY) 02 engineering and technology Simultaneous localization and mapping [INFO.INFO-CG]Computer Science [cs]/Computational Geometry [cs.CG] Electrical Engineering and Systems Science - Systems and Control 01 natural sciences Smoothing methods localization ill-conditionning Computer Science::Robotics Computer Science - Robotics [SPI]Engineering Sciences [physics] 020901 industrial engineering & automation Inertial measurement unit FOS: Electrical engineering electronic engineering information engineering FOS: Mathematics inertial navigation [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO] Electrical and Electronic Engineering Mathematics - Optimization and Control Inertial navigation system Information filtering system ComputingMilieux_MISCELLANEOUS information filter 010401 analytical chemistry Kalman filter 0104 chemical sciences Optimization and Control (math.OC) Control and Systems Engineering A priori and a posteriori [MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC] Robotics (cs.RO) Algorithm Kalman filters Smoothing |
| Zdroj: | IEEE Transactions on Control Systems Technology IEEE Transactions on Control Systems Technology, Institute of Electrical and Electronics Engineers, 2021, 29 (3), pp.1219-1232. ⟨10.1109/TCST.2020.3001387⟩ |
| ISSN: | 1063-6536 |
| DOI: | 10.1109/TCST.2020.3001387⟩ |
| Popis: | International audience; We consider the problem of localizing a manned, semi-autonomous, or autonomous vehicle in the environment using information coming from the vehicle's sensors, a problem known as navigation or simultaneous localization and mapping (SLAM) depending on the context. To infer knowledge from sensors' measurements, while drawing on a priori knowledge about the vehicle's dynamics, modern approaches solve an optimization problem to compute the most likely trajectory given all past observations, an approach known as smoothing. Improving smoothing solvers is an active field of research in the SLAM community. Most work is focused on reducing computation load by inverting the involved linear system while preserving its sparsity. The present paper raises an issue which, to the knowledge of the authors, has not been addressed yet: standard smoothing solvers require explicitly using the inverse of sensor noise covariance matrices. This means the parameters that reflect the noise magnitude must be sufficiently large for the smoother to properly function. When matrices are close to singular, which is the case when using high precision modern inertial measurement units (IMU), numerical issues necessarily arise, especially with 32-bits implementation demanded by most industrial aerospace applications. We discuss these issues and propose a solution that builds upon the Kalman filter to improve smoothing algorithms. We then leverage the results to devise a localization algorithm based on fusion of IMU and vision sensors. Successful real experiments using an actual car equipped with a tactical grade high performance IMU and a LiDAR illustrate the relevance of the approach to the field of autonomous vehicles. |
| Databáze: | OpenAIRE |
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