A generic multi-sensor fusion scheme for localization of autonomous platforms using moving horizon estimation

Abstract

In this paper, a generic multi-sensor fusion framework is developed for the localization of intelligent vehicles and mobile robots. The localization framework is based on moving horizon estimation (MHE). Unlike the commonly used probabilistic filtering algorithms – for example, extended Kalman filter (EKF) and unscented Kalman filter (UKF) – MHE relies on solving successive least squares optimization problems over the innovation of multiple sensors’ measurements and a specific estimation horizon. In this paper, we present an efficient and generic multi-sensor fusion scheme, based on MHE. The proposed multi-sensor fusion scheme is capable of operating with different sensors’ rates, missing measurements, and outliers. Moreover, the proposed scheme is based on a multi-threading architecture to reduce its computational cost, making it more feasible for practical applications. The MHE fusion method is tested using simulated data as well as real experimental data sequences from an intelligent vehicle and a mobile robot combining measurements from different sensors to get accurate localization results. The performance of MHE is compared against that of UKF, where the MHE estimation results show superior performance.

Differentiable Moving Horizon Estimation for Robust Flight Control

“…A straightforward and common practice for constrained MHE is to enforce (23) to be hard constraints, as shown below:…”

“…Moreover, the discontinuous switch between inactive and active inequality constraints may incur numerical instability in learning. Instead of treating (23) as hard constraints, we draw inspiration from interior-point methods to softly penalize (23) in the cost function of the MHE problem (4) by using logarithm barrier functions, i.e., min…”

Design and stability of moving horizon estimator for discrete-time linear systems subject to multiple measurement outliers

“…Moreover, because of its intrinsic robustness, it is also well suited to deal with situations where there are numerical errors (Alessandri et al, 2008). The researches on MHE have been extended from the original linear systems (Alessandri et al, 2003; Osman et al, 2021; Rao et al, 2001) to nonlinear systems (Alessandri et al, 2008; Rao et al, 2003) and hybrid systems (Alessandri et al, 2005; Guo and Huang, 2013; Sun et al, 2019). The MHE affected by outliers was first investigated in Alessandri and Awawdeh (2016), where a special leave-one-out moving horizon estimation strategy was developed to remove the measurements that might be contaminated by outliers.…”

The practical design of a vehicle magnetic signature generator

“…There are various methods for vehicle detection (Ai and Wang, 2016) and localization (Osman et al, 2021) based on different sensors. In this paper, the focus is set on the magnetic field sensor since the magnetic field is omnipresent and relatively stable on a wide area.…”