Multi-camera visual SLAM for autonomous navigation of micro aerial vehicles

Yang, Shaowu; Scherer, Sebastian; Yi, Xiaodong; Zell, Andreas

doi:10.1016/j.robot.2017.03.018

Cited by 79 publications

(35 citation statements)

References 24 publications

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“…where the δ r and δ t are the rotational component and the translational component of ∆c ij , respectively. It can be considered the camera pose constraints generated during the optimization are similar in accuracy [41]. Therefore, ω ij is set to a constant ω ij = 1.…”

Section: Monocular Camera Pose Trackingmentioning

confidence: 99%

SLAM-Based Self-Calibration of a Binocular Stereo Vision Rig in Real-Time

Yin

Ming

et al. 2020

Sensors

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The calibration problem of binocular stereo vision rig is critical for its practical application. However, most existing calibration methods are based on manual off-line algorithms for specific reference targets or patterns. In this paper, we propose a novel simultaneous localization and mapping (SLAM)-based self-calibration method designed to achieve real-time, automatic and accurate calibration of the binocular stereo vision (BSV) rig’s extrinsic parameters in a short period without auxiliary equipment and special calibration markers, assuming the intrinsic parameters of the left and right cameras are known in advance. The main contribution of this paper is to use the SLAM algorithm as our main tool for the calibration method. The method mainly consists of two parts: SLAM-based construction of 3D scene point map and extrinsic parameter calibration. In the first part, the SLAM mainly constructs a 3D feature point map of the natural environment, which is used as a calibration area map. To improve the efficiency of calibration, a lightweight, real-time visual SLAM is built. In the second part, extrinsic parameters are calibrated through the 3D scene point map created by the SLAM. Ultimately, field experiments are performed to evaluate the feasibility, repeatability, and efficiency of our self-calibration method. The experimental data shows that the average absolute error of the Euler angles and translation vectors obtained by our method relative to the reference values obtained by Zhang’s calibration method does not exceed 0.5˚ and 2 mm, respectively. The distribution range of the most widely spread parameter in Euler angles is less than 0.2˚ while that in translation vectors does not exceed 2.15 mm. Under the general texture scene and the normal driving speed of the mobile robot, the calibration time can be generally maintained within 10 s. The above results prove that our proposed method is reliable and has practical value.

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Section: Monocular Camera Pose Trackingmentioning

confidence: 99%

SLAM-Based Self-Calibration of a Binocular Stereo Vision Rig in Real-Time

Yin

Ming

et al. 2020

Sensors

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“…More recently, the emphasis moved to robot coordination and cooperation. Multi-robot systems are mainly composed of three complementary components -perception [42], planning and control [1], and communications [23] that interact together to get a consistent and robust system. One of the main challenges of the perception component is the Simultaneous Localization and Mapping (SLAM) where no global positioning system is used.…”

Section: Introductionmentioning

confidence: 99%

Communicating Multi-UAV System for Cooperative SLAM-based Exploration

Mahdoui

Frémont

Natalizio

2019

J Intell Robot Syst

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In the context of multi-robot system and more generally for Technological System-of-Systems, this paper proposes a multi-UAV (Unmanned Aerial Vehicle) framework for SLAM-based cooperative exploration under limited communication bandwidth. The exploration strategy, based on RGB-D grid mapping and group leader decision making, uses a new utility function that takes into account each robot distance in the group from the unexplored set of targets, and allows to simultaneously explore the environment and to get a detailed grid map of specific areas in an optimized manner. Compared to state-of-the-art approaches, the main novelty is to exchange only the frontier points of the computed local grid map to reduce the shared data volume, and consequently the memory consumption. Moreover, communications constraints are taken into account within a SLAM-based multi-robot collective exploration. In that way, the proposed strategy is also designed to cope with communications drop-out or failures. The multi-UAV system is implemented into ROS and GAZEBO simulators on multiple computers provided with network facilities. Results show that the proposed cooperative exploration strategy minimizes

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“…Localization is a basic ability for mobile robot systems such as self‐driving cars. It is the prerequisite for autonomous navigation of such robots . Traditionally, self‐driving cars use a Global Positioning System (GPS) to provide accurate global position estimations .…”

Section: Introductionmentioning

confidence: 99%

“…It is the prerequisite for autonomous navigation of such robots. 1 Traditionally, self-driving cars use a Global Positioning System (GPS) to provide accurate global position estimations. 2 However, the GPS can suffer from substantial errors in urban canyons and indoor environments where the GPS signal interferes.…”

Section: Introductionmentioning

confidence: 99%

Scale‐aware camera localization in 3D LiDAR maps with a monocular visual odometry

Sun

Yang

Liu

2019

Computer Animation & Virtual

Self Cite

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Localization information is essential for mobile robot systems in navigation tasks. Many visual‐based approaches focus on localizing a robot within prior maps acquired with cameras. It is critical where the Global Positioning System signal is unreliable. In contrast to conventional methods that localize a camera in an image‐based map, we propose a novel approach that localizes a monocular camera within a given three‐dimensional (3D) light detection and ranging (LiDAR) map. We employ visual odometry to reconstruct a semidense set of 3D points from the monocular camera images. These points are continuously matched against the 3D prior LiDAR map by a modified feature‐based point cloud registration method to track a full six‐degree‐of‐freedom camera pose. Since the monocular camera suffers from the scale‐drift problem due to the lack of depth information, the proposed method solves it by adopting updatable scale estimation. Experiments carried out on a publicly large‐scale data set demonstrate that the camera and LiDAR multimodal data matching problem is solved, and the localization accuracy of our method is comparable to state‐of‐the‐art approaches.

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Multi-camera visual SLAM for autonomous navigation of micro aerial vehicles

Cited by 79 publications

References 24 publications

SLAM-Based Self-Calibration of a Binocular Stereo Vision Rig in Real-Time

SLAM-Based Self-Calibration of a Binocular Stereo Vision Rig in Real-Time

Communicating Multi-UAV System for Cooperative SLAM-based Exploration

Scale‐aware camera localization in 3D LiDAR maps with a monocular visual odometry

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