Loosely-Coupled Ultra-wideband-Aided Scale Correction for Monocular Visual Odometry

Nguyen, Thien‐Minh; Cao, Muqing; Xie, Lihua

doi:10.1142/s2301385020500119

Cited by 23 publications

(8 citation statements)

References 36 publications

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“…It is observed that the detected saliency with high contrast and more edges is often unique and highly distinguishable, which is suitable for place recognition. Considering the computational cost, we adopt a discriminative strategy using contrast density φ x and edge complexity ρ x defined in (3).…”

Section: A Saliency Detectionmentioning

confidence: 99%

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Online Visual Place Recognition via Saliency Re-identification

Wang

Xie

2020

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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As an essential component of visual simultaneous localization and mapping (SLAM), place recognition is crucial for robot navigation and autonomous driving. Existing methods often formulate visual place recognition as feature matching, which is computationally expensive for many robotic applications with limited computing power, e.g., autonomous driving and cleaning robot. Inspired by the fact that human beings always recognize a place by remembering salient regions or landmarks that are more attractive or interesting than others, we formulate visual place recognition as saliency reidentification. In the meanwhile, we propose to perform both saliency detection and re-identification in frequency domain, in which all operations become element-wise. The experiments show that our proposed method achieves competitive accuracy and much higher speed than the state-of-the-art feature-based methods. The proposed method is open-sourced and available at https://github.com/wh200720041/SRLCD.git.

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Section: A Saliency Detectionmentioning

confidence: 99%

“…It is of great importance for generating drift-free maps in simultaneous localization and mapping (SLAM). In robot navigation, trajectory estimation often comes with drifts due to the sensor imperfection and environmental variation [3]. Without the capability of loop closure, estimated robot pose inevitably deviates from its true position.…”

Section: Introductionmentioning

confidence: 99%

Online Visual Place Recognition via Saliency Re-identification

Wang

Xie

2020

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…In [28], a loosely coupled method was developed to correct the scale of a monocular visual odometry (VO) output using the distance measurements from a single UWB anchor. In this method the anchor's position is also estimated with respect to the coordinate frame of the VO system, whose origin coincides with the initial position of the camera.…”

Section: Related Workmentioning

confidence: 99%

LIRO: Tightly Coupled Lidar-Inertia-Ranging Odometry

Cao¹,

Yuan²,

Lyu³

et al. 2020

Preprint

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In recent years, thanks to the continuously reduced cost and weight of 3D Lidar, the applications of this type of sensor in robotics community have become increasingly popular. Despite many progresses, estimation drift and tracking loss are still prevalent concerns associated with these systems. However, in theory these issues can be resolved with the use of some observations to fixed landmarks in the environments. This motivates us to investigate a tightly coupled sensor fusion scheme of Ultra-Wideband (UWB) range measurements with Lidar and inertia measurements. First, data from IMU, Lidar and UWB are associated with the robot's states on a sliding windows based on their timestamps. Then, we construct a cost function comprising of factors from UWB, Lidar and IMU preintegration measurements. Finally an optimization process is carried out to estimate the robot's position and orientation. It is demonstrated through some real world experiments that the method can effectively resolve the drift issue, while only requiring two or three anchors deployed in the environment.

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“…other SLAM system such as visual SLAM [6], [7] and visual inertial SLAM [8], [9]. Those properties have made LiDAR SLAM widely applied to various robotic applications such as autonomous driving [10], building inspection [11], and intelligent manufacturing [12].…”

Section: Introductionmentioning

confidence: 99%

Lightweight 3-D Localization and Mapping for Solid-State LiDAR

Wang,

Xie

2021

Preprint

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The LIght Detection And Ranging (LiDAR) sensor has become one of the most important perceptual devices due to its important role in simultaneous localization and mapping (SLAM). Existing SLAM methods are mainly developed for mechanical LiDAR sensors, which are often adopted by large scale robots. Recently, the solid-state LiDAR is introduced and becomes popular since it provides a cost-effective and lightweight solution for small scale robots. Compared to mechanical LiDAR, solid-state LiDAR sensors have higher update frequency and angular resolution, but also have smaller field of view (FoV), which is very challenging for existing LiDAR SLAM algorithms. Therefore, it is necessary to have a more robust and computationally efficient SLAM method for this new sensing device. To this end, we propose a new SLAM framework for solidstate LiDAR sensors, which involves feature extraction, odometry estimation, and probability map building. The proposed method is evaluated on a warehouse robot and a hand-held device. In the experiments, we demonstrate both the accuracy and efficiency of our method using an Intel L515 solid-state LiDAR. The results show that our method is able to provide precise localization and high quality mapping. We made the source codes public at https://github.com/wh200720041/SSL SLAM.

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Loosely-Coupled Ultra-wideband-Aided Scale Correction for Monocular Visual Odometry

Cited by 23 publications

References 36 publications

Online Visual Place Recognition via Saliency Re-identification

Online Visual Place Recognition via Saliency Re-identification

LIRO: Tightly Coupled Lidar-Inertia-Ranging Odometry

Lightweight 3-D Localization and Mapping for Solid-State LiDAR

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