Towards Noise Resilient SLAM

Thyagharajan, Anirud; Omer, Om Ji; Mandal, Dipan Kumar; Subramoney, Sreenivas

doi:10.1109/icra40945.2020.9196745

Cited by 3 publications

(1 citation statement)

References 48 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]. The performance of a SLAM system through the use of a novel, generic, and extendable framework for quantitative analysis and comparison of SLAM datasets is discussed in [39].…”

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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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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Multi-Parameter Optimization for a Robust RGB-D SLAM System

Wang

Zhu

et al. 2021

2021 IEEE International Conference on Robotics and Automation (ICRA)

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Towards Noise Resilient SLAM

Cited by 3 publications

References 48 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

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

Multi-Parameter Optimization for a Robust RGB-D SLAM System

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