Deterministic initialization of metric state estimation filters for loosely-coupled monocular vision-inertial systems

Kneip, Laurent; Weiß, Stephan; Siegwart, Roland

doi:10.1109/iros.2011.6094699

Cited by 42 publications

(21 citation statements)

References 26 publications

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“…Robust, fast initialization to provide of accurate initial state estimates is crucial to bootstrap real-time VINS estimators, which is often solved in a linear closed form [7,84,120,121,122,123,124]. In particular, Martinelli [123] introduced a closed-form solution to the monocular visual-inertial initialization problem and later extended to the case where gyroscope bias calibration is also included [125] as well as to the cooperative scenario [126].…”

Section: State Initializationmentioning

confidence: 99%

Visual-Inertial Navigation: A Concise Review

Huang

2019

2019 International Conference on Robotics and Automation (ICRA)

299

119

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As inertial and visual sensors are becoming ubiquitous, visual-inertial navigation systems (VINS) have prevailed in a wide range of applications from mobile augmented reality to aerial navigation to autonomous driving, in part because of the complementary sensing capabilities and the decreasing costs and size of the sensors. In this paper, we survey thoroughly the research efforts taken in this field and strive to provide a concise but complete review of the related work -which is unfortunately missing in the literature while being greatly demanded by researchers and engineers -in the hope to accelerate the VINS research and beyond in our society as a whole.where δq describes the small rotation that causes the true and estimated attitude to coincide. The advantage of this parametrization permits a minimal representation, 3 × 3 covariance matrix E I θ I θ T , for the attitude uncertainty.

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Section: State Initializationmentioning

confidence: 99%

Visual-Inertial Navigation: A Concise Review

Huang

2019

2019 International Conference on Robotics and Automation (ICRA)

299

119

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“…Therefore the initial state estimation is very important and attracts great interest among researchers. The early paper [ 27 ] presents a deterministic closed-form solution to compute the gravity and the visual scale and provide the initial metric values for the state estimation filter. However as a result of the lack of IMU biases, the estimated scale and gravity are not accurate, which results in a poor system stability.…”

Section: Related Workmentioning

confidence: 99%

Accurate Initial State Estimation in a Monocular Visual–Inertial SLAM System

Chen

Zhou

et al. 2018

Sensors

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The fusion of monocular visual and inertial cues has become popular in robotics, unmanned vehicles and augmented reality fields. Recent results have shown that optimization-based fusion strategies outperform filtering strategies. Robust state estimation is the core capability for optimization-based visual–inertial Simultaneous Localization and Mapping (SLAM) systems. As a result of the nonlinearity of visual–inertial systems, the performance heavily relies on the accuracy of initial values (visual scale, gravity, velocity and Inertial Measurement Unit (IMU) biases). Therefore, this paper aims to propose a more accurate initial state estimation method. On the basis of the known gravity magnitude, we propose an approach to refine the estimated gravity vector by optimizing the two-dimensional (2D) error state on its tangent space, then estimate the accelerometer bias separately, which is difficult to be distinguished under small rotation. Additionally, we propose an automatic termination criterion to determine when the initialization is successful. Once the initial state estimation converges, the initial estimated values are used to launch the nonlinear tightly coupled visual–inertial SLAM system. We have tested our approaches with the public EuRoC dataset. Experimental results show that the proposed methods can achieve good initial state estimation, the gravity refinement approach is able to efficiently speed up the convergence process of the estimated gravity vector, and the termination criterion performs well.

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“…This also resolves the absolute scale problem of monocular SLAM. The theoretical background for this is described in [3], [4], [8], [9], [11], [13], [14], [15]. The system is able to navigate autonomously by waypoint following.…”

Section: Extended Summarymentioning

confidence: 99%

SFly: Swarm of micro flying robots

Achtelik

Achtelik²,

Brunet

et al. 2012

2012 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-The SFly project is an EU-funded project, with the goal to create a swarm of autonomous vision controlled micro aerial vehicles. The mission in mind is that a swarm of MAV's autonomously maps out an unknown environment, computes optimal surveillance positions and places the MAV's there and then locates radio beacons in this environment. The scope of the work includes contributions on multiple different levels ranging from theoretical foundations to hardware design and embedded programming. One of the contributions is the development of a new MAV, a hexacopter, equipped with enough processing power for onboard computer vision. A major contribution is the development of monocular visual SLAM that runs in real-time onboard of the MAV. The visual SLAM results are fused with IMU measurements and are used to stabilize and control the MAV. This enables autonomous flight of the MAV, without the need of a data link to a ground station. Within this scope novel analytical solutions for fusing IMU and vision measurements have been derived. In addition to the realtime local SLAM, an offline dense mapping process has been developed. For this the MAV's are equipped with a payload of a stereo camera system. The dense environment map is used to compute optimal surveillance positions for a swarm of MAV's. For this an optimiziation technique based on cognitive adaptive optimization has been developed. Finally, the MAV's have been equipped with radio transceivers and a method has been developed to locate radio beacons in the observed environment. I. EXTENDED SUMMARYThe goal of the SFly project [1] was to create a swarm of autonomous Micro Aerial Vehicles (MAV's) for applications in search and rescue missions. This video (additional videos are available on the SFly Youtube Channel [2]) demonstrates the use of the developed MAV's in a simulated disaster response situation. In the demonstrated mission, the MAV's are used to provide an aerial overview of the disaster scene and to locate victims.In the first step of the mission a swarm of 3 MAV's autonomously explores the environment and captures aerial image data which is used to compute a 3D model of the (Fig. 1). The SFly MAV (developed by the project partner Ascending Technologies) consists of a hexacopter base with a diameter of around 55cm [16]. It is equipped with an IMU for attitude control as well as pressure sensor and GPS. A highlight is the onboard computer, an Intel Core2Duo, which is powerful enough to do the real-time image processing of the onboard cameras. The MAV is equipped with a downward looking monocular camera which is used for flight control and a configurable stereo setup that can be either used for mapping or obstacle detection. The weight of the system is 1.5kg.For flight control a local visual SLAM algorithm is running onboard and in real-time using the downward looking monocular camera. This allows stable hovering and also takeoff and landing maneuvers [17], [5], [12]. The full state of the MAV is computed by fusing the visual SLAM poses with measu...

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Deterministic initialization of metric state estimation filters for loosely-coupled monocular vision-inertial systems

Cited by 42 publications

References 26 publications

Visual-Inertial Navigation: A Concise Review

Visual-Inertial Navigation: A Concise Review

Accurate Initial State Estimation in a Monocular Visual–Inertial SLAM System

SFly: Swarm of micro flying robots

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