Visual-Inertial SLAM with Tightly-Coupled Dropout-Tolerant GPS Fusion

Boche, Simon; Zuo, Xingxing; Schaefer, Simon; Leutenegger, Stefan

doi:10.1109/iros47612.2022.9981134

Cited by 11 publications

(5 citation statements)

References 19 publications

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“…An important consideration is the modelling of the noise of GNSS measurements. Based on previ- ous works (Cioffi & Scaramuzza, 2020;Boche et al, 2022), the uncertainty was modelled as additive isotropic Gaussian noise. This is a simple model that arises naturally from the GNSS device data, as the device drivers generally provide a covariance of the position.…”

Section: Discussionmentioning

confidence: 99%

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GNSS‐stereo‐inertial SLAM for arable farming

Cremona

Civera

Kofman

et al. 2023

Journal of Field Robotics

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The accelerating pace in the automation of agricultural tasks demands highly accurate and robust localization systems for field robots. Simultaneous Localization and Mapping (SLAM) methods inevitably accumulate drift on exploratory trajectories and primarily rely on place revisiting and loop closing to keep a bounded global localization error. Loop closure techniques are significantly challenging in agricultural fields, as the local visual appearance of different views is very similar and might change easily due to weather effects. A suitable alternative in practice is to employ global sensor positioning systems jointly with the rest of the robot sensors. In this paper we propose and implement the fusion of global navigation satellite system (GNSS), stereo views, and inertial measurements for localization purposes. Specifically, we incorporate, in a tightly coupled manner, GNSS measurements into the stereo‐inertial ORB‐SLAM3 pipeline. We thoroughly evaluate our implementation in the sequences of the Rosario data set, recorded by an autonomous robot in soybean fields, and our own in‐house data. Our data includes measurements from a conventional GNSS, rarely included in evaluations of state‐of‐the‐art approaches. We characterize the performance of GNSS‐stereo‐inertial SLAM in this application case, reporting pose error reductions between 10% and 30% compared to visual–inertial and loosely coupled GNSS‐stereo‐inertial baselines. In addition to such analysis, we also release the code of our implementation as open source.

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Section: Discussionmentioning

confidence: 99%

“…The authors proposed a GPS-IMU online calibration approach for the time offset and extrinsics estimation. In (Boche et al, 2022) a tightly coupled visual-inertial-GPS system is presented. The system is based on OKVIS2 (Leutenegger, 2022).…”

Section: Related Workmentioning

confidence: 99%

GNSS‐stereo‐inertial SLAM for arable farming

Cremona

Civera

Kofman

et al. 2023

Journal of Field Robotics

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“…[18], [22], [23] adopt explicitly online calibration of the rotational extrinsic parameter to improve localization accuracy.…”

Section: Related Workmentioning

confidence: 99%

“…To simplify the state estimation complexity, [23] disables the online estimation once the rotational extrinsic parameter is converged. However, neither of them provide a theoretical observability analysis.…”

Section: Related Workmentioning

confidence: 99%

Towards Online System Identification: Benchmark of Model Identification Techniques for Variable Dynamics UAV Applications

Song

Sanchez-Cuevas

Olivares-Méndez

2022

2022 International Conference on Unmanned Aircraft Systems (ICUAS)

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Providing self-modelling capabilities to robotic systems that could change their dynamics variables during their natural operation, like aerial manipulators, can significantly increase model-based algorithm's resilience and performance. Some samples of those techniques are model predictive control or model-based trajectory planning techniques. This paper aims to benchmark how classical model identification techniques perform when adapted to be used at run-time. This self-awareness capability will improve transparency giving the robot the capability to understand what it can do and how to perform optimally. To do so, this paper compares four different model identification techniques and how they perform online in terms of accuracy and computational cost. The online adaptation of the model identification methods has been developed generically to apply to any dynamic system. The methods are numerically evaluated simulating an Unmanned Aerial Vehicle (UAV) with time-varying mass. The system has been evaluated in 5 different manoeuvres with 5 different mass-behaviour. This creates 25 experiments for each of the four model identification algorithms. In total, this paper presents the result of 100 studied cases.

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“…Approaches that integrate GNSS with SLAM-VIO-based methods have recently been investigated [9][10][11][12], but they are in the early stages. One recent effort in this direction is the work carried out in the IOPES project [8].…”

Section: Introductionmentioning

confidence: 99%

Enhanced Seamless Indoor–Outdoor Tracking Using Time Series of GNSS Positioning Errors

Angelats,

Gorreja,

Espín-López

et al. 2024

IJGI

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The seamless integration of indoor and outdoor positioning has gained considerable attention due to its practical implications in various fields. This paper presents an innovative approach aimed at detecting and delineating outdoor, indoor, and transition areas using a time series analysis of Global Navigation Satellite System (GNSS) error statistics. By leveraging this contextual understanding, the decision-making process between GNSS-based and Visual-Inertial Odometry (VIO) for trajectory estimation is refined, enabling a more robust and accurate positioning. The methodology involves three key steps: proposing the division of our context environment into a set of areas (indoor, outdoor, and transition), exploring two methodologies for the classification of space based on a time series of GNSS error statistics, and refining the trajectory estimation strategy based on contextual knowledge. Real data across diverse scenarios validate the approach, yielding trajectory estimations with accuracy consistently below 10 m.

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Visual-Inertial SLAM with Tightly-Coupled Dropout-Tolerant GPS Fusion

Cited by 11 publications

References 19 publications

GNSS‐stereo‐inertial SLAM for arable farming

GNSS‐stereo‐inertial SLAM for arable farming

Towards Online System Identification: Benchmark of Model Identification Techniques for Variable Dynamics UAV Applications

Enhanced Seamless Indoor–Outdoor Tracking Using Time Series of GNSS Positioning Errors

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