Towards Globally Consistent Visual-Inertial Collaborative SLAM

Karrer, Marco; Chli, Margarita

doi:10.1109/icra.2018.8461213

Cited by 8 publications

(9 citation statements)

References 21 publications

(39 reference statements)

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“…Existing techniques for perception in aerial manipulation are, in most cases, methods adapted from aerial vehicles without manipulation skills to provide positional accuracy and place recognition [28]. The AEROARMS I&M usecases require however, perception modules that go beyond the state of the art to: (1) accurately localize the vehicle, both during the navigation and manipulation phases; (2) localize (3) detect the objects involved in the I&M tasks and; (4) pick up or release the crawler by the aerial robot.…”

Section: Perceptionmentioning

confidence: 99%

The AEROARMS Project: Aerial Robots with Advanced Manipulation Capabilities for Inspection and Maintenance

Ollero

Heredia

Franchi

et al. 2018

IEEE Robot. Automat. Mag.

177

124

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This paper summarizes new aerial robotic manipulation technologies and methods, required for outdoor industrial inspection and maintenance, developed in the AEROARMS project. It presents aerial robotic manipulators with dual arms and multi-directional thrusters. It deals with the control systems, including the control of the interaction forces and the compliance, the teleoperation, which uses passivity to tackle the tradeoff between stability and performance, perception methods for localization, mapping and inspection, and planning methods, including a new control-aware approach for aerial manipulation. Finally, it describes a novel industrial platform with multidirectional thrusters and a new arm design to increase the robustness in industrial contact inspections. The lessons learned in the application to outdoor aerial manipulation for inspection and maintenance are pointed out.

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Section: Perceptionmentioning

confidence: 99%

The AEROARMS Project: Aerial Robots with Advanced Manipulation Capabilities for Inspection and Maintenance

Ollero

Heredia

Franchi

et al. 2018

IEEE Robot. Automat. Mag.

177

124

View full text Add to dashboard Cite

show abstract

“…Alongside the emergence of vision based SLAM for single robots [2], [16], [18], [22], research into multi-robot systems has been attracting increasing attention recently. The collaborative frameworks proposed in [5] and [11] demonstrate global mapping from Keyframe (KF) data obtained from multiple UAVs. Other systems aim to distribute the parts within the SLAM estimation process across the agents and a cental server [13], [23], [25], promising to reduce the computational load for the agents and to make map data generated from each agent available to the rest of the robotic team.…”

Section: Related Workmentioning

confidence: 99%

“…The terms y k,j are the reprojection residuals as defined in Eq. ( 10) and (11) with the corresponding weights given by W k,j r = σ −2 obs • I 2×2 . The set of relative distance measurements with standard deviation σ d between the two agents is denoted by D, while the corresponding residual terms are given by…”

Section: E Optimization Back-endmentioning

confidence: 99%

Distributed Variable-Baseline Stereo SLAM from two UAVs

Karrer

Chli

2021

2021 IEEE International Conference on Robotics and Automation (ICRA)

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Visual-Inertial Odometry (VIO) has been widely used and researched to control and aid the automation of navigation of robots especially in the absence of absolute position measurements, such as GPS. However, when the observable landmarks in the scene lie far away, as in highaltitude flights for example, the fidelity of the metric scale estimate in VIO greatly degrades. Aiming to tackle this issue, in this work, we utilize the virtual stereo setup formed by two Unmanned Aerial Vehicles (UAVs), equipped with one camera and one Inertial Measurement Unit (IMU) each, exploiting their view overlap and relative distance measurements between them using onboard Ultra-Wideband (UWB) modules to enable collaborative VIO. In particular, we propose a decentralized collaborative estimation scheme, where each agent holds its own local map, achieving a low pose estimation latency, while ensuring consistency of each agents' estimates via consensus-based optimization. Following a thorough evaluation in photorealistic simulations, we demonstrate the effectiveness of the approach at high-altitude flights of up to 160m, going significantly beyond the capabilities of state-of-the-art VIO methods. Finally, we show the advantage of actively adjusting the baseline on-the-fly over a fixed, target baseline, resulting in a significant reduction of the estimation error.Videohttps://youtu.be/SdL4Jb-BQ28

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“…The other research stream [34][35][36] is the overlap detectionbased collaborative positioning where the exteroceptive sensors, such as the 3D light detection and ranging (LiDAR) or camera, are employed to detect the overlapping area to further formulate the connection between agents. The work in [37] proposed a collaborative visual simultaneous localization and mapping (SLAM) framework to enhance the accuracy of state estimation of each robot equipped with a monocular camera.…”

Section: Introductionmentioning

confidence: 99%

“…However, the robustness and accuracy are limited simply based on the monocular camera. The work in [36,39] employs the IMU to help to estimate the scaling and perform VINS in each agent. A similar overlapping area detection scheme is employed to establish the inter-agent connections.…”

Section: Introductionmentioning

confidence: 99%

Multi-Agent Collaborative GNSS/Camera/INS Integration Aided by Inter-Ranging for Vehicular Navigation in Urban Areas

et al. 2020

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Achieving accurate and reliable positioning in dynamic urban scenarios using low-cost vehicular onboard sensors, such as the global navigation satellite systems (GNSS), camera, and inertial measurement unit (IMU), is still a challenging problem. Multi-Agent collaborative integration (MCI) opens a new window for achieving this goal, by sharing the sensor measurements between multiple agents to further improve the accuracy of respective positioning. One of the major difficulties in MCI is to effectively connect all the sensor measurements arising from multiple independent agents. The popular approach is to find the overlapping areas between agents using active sensors, such as cameras. However, the performance of overlapping area detection is significantly degraded in outdoor urban areas due to the challenges arising from numerous unexpected moving objects and unstable illumination conditions. To fill this gap, this paper proposes to leverage both the camera-based overlapping area detection and the inter-ranging measurements to boost the cross-connection between multi-agents and brings the MCI to outdoor urban scenarios using lowcost onboard sensors. Moreover, a novel MCI framework is proposed to integrate the sensor measurements from the low-cost GNSS receiver, camera, IMU, and inter-ranging using state-of-the-art factor graph optimization (FGO) to fully explore their complementary properties. The proposed MCI framework is validated using two challenging datasets collected in urban canyons of Hong Kong. We conclude that the proposed MCI framework can effectively improve the positioning accuracy of the respective agents in the evaluated datasets. We believe that the proposed MCI framework has the potential to be prevalently adopted by the connected intelligent transportation systems (ITS) applications to provide robust positioning using low-cost onboard sensors in urban scenarios.

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Towards Globally Consistent Visual-Inertial Collaborative SLAM

Cited by 8 publications

References 21 publications

The AEROARMS Project: Aerial Robots with Advanced Manipulation Capabilities for Inspection and Maintenance

The AEROARMS Project: Aerial Robots with Advanced Manipulation Capabilities for Inspection and Maintenance

Distributed Variable-Baseline Stereo SLAM from two UAVs

Multi-Agent Collaborative GNSS/Camera/INS Integration Aided by Inter-Ranging for Vehicular Navigation in Urban Areas

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