Robust Visual-Lidar Simultaneous Localization and Mapping System for UAV

Qian, Jie; Chen, Kaiqi; Chen, Qinying; Yang, Yanhong; Zhang, Jianhua; Chen, Shengyong

doi:10.1109/lgrs.2021.3099166

Cited by 25 publications

(17 citation statements)

References 24 publications

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“…More recently, Qian et al [10] presented a multisensor UAV payload and SLAM algorithm that is based on the fusion of Velodyne LiDAR data and RGB imagery. More specifically, they extract line and plane features from the LiDAR data and use them to compute the relative pose between consecutive frames in an ICP-based manner.…”

Section: Related Workmentioning

confidence: 99%

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A Multisensor UAV Payload and Processing Pipeline for Generating Multispectral Point Clouds

et al. 2023

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Over the last two decades, UAVs have become an indispensable acquisition platform in the remote sensing community. Meanwhile, advanced lightweight sensors have been introduced in the market, including LiDAR scanners with multiple beams and hyperspectral cameras measuring reflectance using many different narrow-banded filters. To date, however, few fully fledged drone systems exist that combine different sensing modalities in a way that complements the strengths and weaknesses of each. In this paper, we present our multimodal drone payload and sensor fusion pipeline, which allows multispectral point clouds to be generated at subcentimeter accuracy. To that end, we combine high-frequency navigation outputs from a professional-grade GNSS with photogrammetric bundle adjustment and a dedicated point cloud registration algorithm that takes full advantage of LiDAR’s specifications. We demonstrate that the latter significantly improves the quality of the reconstructed point cloud in terms of fewer ghosting effects and less noise. Finally, we thoroughly discuss the impact of the quality of the GNSS/INS system on the structure from the motion and LiDAR SLAM reconstruction process.

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Section: Related Workmentioning

confidence: 99%

“…Note that our LiDAR and GNSS/INS-based self-localization itself is running in real-time. Finally, compared to [10], our approach allows fusing data from different imaging modalities such as a multispectral, hyperspectral or thermal camera, which usually have a limited and different spatial resolution.…”

Section: Related Workmentioning

confidence: 99%

A Multisensor UAV Payload and Processing Pipeline for Generating Multispectral Point Clouds

et al. 2023

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“…A wide range of methods has been pursued to solve this problem. Most preliminary methods use LiDAR [9][10][11] or cameras to perceive the environment. The LiDAR sensors have a wide detection range and can directly provide high-precision depth information.…”

Section: Localizationmentioning

confidence: 99%

An End-to-End UAV Simulation Platform for Visual SLAM and Navigation

et al. 2022

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Visual simultaneous localization and mapping (v-SLAM) and navigation of unmanned aerial vehicles (UAVs) are receiving increasing attention in both research and education. However, extensive physical testing can be expensive and time-consuming due to safety precautions, battery constraints, and the complexity of hardware setups. For the efficient development of navigation algorithms and autonomous systems, as well as for education purposes, the ROS-Gazebo-PX4 simulator was customized in-depth, integrated into our previous released research works, and provided as an end-to-end simulation (E2ES) solution for UAV, v-SLAM, and navigation applications. Unlike most other similar works, which can only stimulate certain parts of the navigation algorithms, the E2ES platform simulates all of the localization, mapping, and path-planning kits in one simulator. The navigation stack performs well in the E2ES test bench with the absolute pose errors of 0.3 m (translation) and 0.9 degree (rotation), respectively, for an 83 m length trajectory. Moreover, the E2ES provides an out-of-box, click-and-fly autonomy in UAV navigation. The project source code is opened for the benefit of the research community.

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“…But, considering the accuracy degradation caused by the error accumulation, it often serves as part of the sensor fusion approach for precise UAV positioning. Furthermore, light detection and ranging (LiDAR) [9] and ultrasonic [10] based localisation technologies have also attracted lots of attention in this area, due to the enhanced positioning accuracy. Yet, the critical issues including the extremely high system cost, weight and energy consumption for LiDAR, limited localisation coverage and vulnerable to the unpredictable signal occlusion for ultrasonic will restrict their applications on UAV positioning [11].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Extended Kalman Filter-Based Fusion Approach for High-Precision UAV Positioning in Extremely Confined Environments

Yang

et al. 2023

IEEE/ASME Trans. Mechatron.

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For unmanned aerial vehicle (UAV) based smart inspection in extremely confined environments, it is impossible for precise UAV positioning with global positioning system (GPS), owing to the satellite signal block. Therefore, the ultra-wideband (UWB) based technology has attracted extensive attention under such circumstances. However, due to the unpredictable propagation condition and the time-varying operational environment, the localisation performance oscillation caused by the changing measurement noise may lead to the instability of UAV. To mitigate the effects, in this paper, a high precision UAV positioning system which integrates the inertial measurement unit (IMU) and UWB with the adaptive extended Kalman filter (AEKF) is proposed. Compared with the traditional EKF based approach, the estimated and recorded information from previous processes is exploited to adaptively estimate and further control the estimation of the noise covariance matrices for performance improvement. Finally, simulations and experiments have been conducted in extremely confined environments. According to the results, the proposed algorithm can significantly improve the position update rate, the median positioning error, the 95 th percentile positioning error and the average standard deviation (STD) into 88Hz, 0.102m, 0.192m and 0.052m, which is applicable for applications in focused environments.Index Terms-Unmanned aerial vehicle (UAV), adaptive extended Kalman filter (AEKF), ultra-wideband (UWB), inertial measurement unit (IMU), sensor fusion, extremely confined environments.

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Robust Visual-Lidar Simultaneous Localization and Mapping System for UAV

Cited by 25 publications

References 24 publications

A Multisensor UAV Payload and Processing Pipeline for Generating Multispectral Point Clouds

A Multisensor UAV Payload and Processing Pipeline for Generating Multispectral Point Clouds

An End-to-End UAV Simulation Platform for Visual SLAM and Navigation

Adaptive Extended Kalman Filter-Based Fusion Approach for High-Precision UAV Positioning in Extremely Confined Environments

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