A resilient solution to Range-Only SLAM based on a decoupled landmark range and bearing reconstruction

Martinelli, Francesco; Mattogno, Simone; Romanelli, Fabrizio

doi:10.1016/j.robot.2022.104324

Cited by 9 publications

(8 citation statements)

References 13 publications

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“…Other important works in the field of indoor positioning and SLAM are represented by [36] and [37], where the authors, considering SLAM problems, propose a multihypothesis extended Kalman filter (MHEKF) techniques. A technique based on Map-Point Consensus-based Outlier Rejection (MC-OR) for sparse-indirect SLAM systems is provided in [38].…”

Section: B Indoor Positioningmentioning

confidence: 99%

CM-SLASM: A Cooperative Multitechnology Simultaneous Localization and Signal Mapping for Vehicles Indoor Positioning

D’Alfonso,

De Rango,

Fedele

et al. 2024

IEEE Sensors J.

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In this paper, a Cooperative Multi-technology Simultaneous Localization and Signal Mapping (CM-SLASM) technique is proposed to improve the signal map accuracy and to build on-thefly a signal map to be used also in indoor environments where conditions can change over time. Moreover, the CM-SLASM combines WiFi, Ultra WideBand (UWB) and LIght Detection And Ranging (LIDAR) signals to improve positioning estimation by sharing information and cooperation among vehicles through Vehicle-To-Vehicle (V2V) communication links. In particular, LIDAR-based distance between vehicles is shared among neighbor vehicles to improve the vehicle positioning estimated by an Extended Kalman Filter (EKF) where WiFi fingerprinting is combined with UWB multilateration. The overall solution where EKF estimation allows to build more precise signal MAP is validated by simulation in a defined indoor scenario where vehicles equipped with different percentages of LIDAR, and different quantities of UWB and WiFi emitters have been considered. The proposed strategy has been validated through an extensive simulation campaign in various scenarios of interest and through a real-world experiment conducted in a laboratory test environment.

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Section: B Indoor Positioningmentioning

confidence: 99%

CM-SLASM: A Cooperative Multitechnology Simultaneous Localization and Signal Mapping for Vehicles Indoor Positioning

D’Alfonso,

De Rango,

Fedele

et al. 2024

IEEE Sensors J.

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“…We first analyze the case where sign( u(k), w(k) ) = 1 and thus u(k), w(k) ≥ c u(k) . Note that the estimator in ( 12) can be expressed as a nonlinear system as in (6). In order to show that ( 12) is a contracting system, we can analyze the corresponding variational system following Lemma 1.…”

Section: A Contraction Analysismentioning

confidence: 99%

“…Various methods have been proposed in the literature to solve the problem using extended Kalman filter SLAM (EKF-SLAM) [3], [6], [7], [8], [9], unscented Kalman filter SLAM (UKF-SLAM) [1] and particle filter SLAM (PF-SLAM) [10]. These methods work well locally, which can introduce a practical issue in their deployment.…”

mentioning

confidence: 99%

“…Namely, they require a good initial estimate of the relative position in order to guarantee the convergence of the algorithms. In general, there are two approaches for the initialization in literature: the delayed and undelayed approaches [6]. In the first approach, the application of the estimation filter is delayed until consistent estimates of the positions for the landmarks are obtained through multiple measurements (such as, the triangulation techniques [3], [7]).…”

mentioning

confidence: 99%

“…The second approach relies on the availability and evaluation of multiple probable hypotheses in order to allow for immediate initialization of the landmarks' relative position and for subsequent application of the estimation filter. The optimization process during such pre-processing step and the recursive computation in these SLAM filters can lead to high computational load [6], [8], [9], [10].…”

mentioning

confidence: 99%

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Range-Only Bearing Estimator for Localization and Mapping

Marcantoni

Jayawardhana

Bunte

2023

IEEE Control Syst. Lett.

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Navigation and exploration within unknown environments are typical examples in which simultaneous localization and mapping (SLAM) algorithms are applied. When mobile agents deploy only range sensors without bearing information, the agents must estimate the bearing using the online distance measurement for the localization and mapping purposes. In this letter, we propose a scalable dynamic bearing estimator to obtain the relative bearing of the static landmarks in the local coordinate frame of a moving agent in real-time. Using contraction theory, we provide convergence analysis of the proposed range-only bearing estimator and present upper and lower-bound for the estimator gain. Numerical simulations demonstrate the effectiveness of the proposed method.

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Resilient Simultaneous Localization and Mapping Fusing Ultra Wide Band Range Measurements and Visual Odometry

Romanelli,

Martinelli,

Mattogno

2023

J Intell Robot Syst

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In this paper we consider a Simultaneous Localization and Mapping (SLAM) problem for a moving agent using Visual Odometry (VO) while measuring the range from a set of Ultra Wide Band (UWB) antennas, deployed in unknown position in the environment. The solution approach is based on a switching observer which, under standard working conditions, for each observed UWB, uses a two dimensional Extended Kalman Filter (EKF) providing an estimate of the range and bearing of the observed UWB with respect to the agent. This information is then used in a Robust EKF algorithm which solves the SLAM problem with performances that, even before closing the loop, are comparable to the ones that a VO algorithm (namely ORB-SLAM2) would obtain only after closing the loop. Moreover, a resilient module is added to the algorithm to evaluate the reliability of the position estimate of each observed UWB. When the Visual Odometry is not available, the switching observer uses an auxiliary EKF to provide an estimate of the agent position. This makes the proposed approach robust with respect to several kinds of unmodeled disturbances, like multipath effects, and automatically adapts to sensor failures with resilience (e.g. when Visual Odometry or UWB measurements are not available).

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A resilient solution to Range-Only SLAM based on a decoupled landmark range and bearing reconstruction

Cited by 9 publications

References 13 publications

CM-SLASM: A Cooperative Multitechnology Simultaneous Localization and Signal Mapping for Vehicles Indoor Positioning

CM-SLASM: A Cooperative Multitechnology Simultaneous Localization and Signal Mapping for Vehicles Indoor Positioning

Range-Only Bearing Estimator for Localization and Mapping

Resilient Simultaneous Localization and Mapping Fusing Ultra Wide Band Range Measurements and Visual Odometry

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