Large-Scale Outdoor SLAM Based on 2D Lidar

Ren, Ruike; Fu, Hao; Wu, Meiping

doi:10.3390/electronics8060613

Cited by 40 publications

(25 citation statements)

References 31 publications

(58 reference statements)

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“…SLAM relies on the richness of lighting and environment texture, however, most indoor environments are not rich in texture, which will affect the accuracy and reliability of the positioning scheme. In addition, there are some positioning schemes based on lidar, such as Gmapping [12], Hector [13], Cartographer [14]. The lidar applied to the quadrotor UAV is generally a singleline lidar due to its large weight, which can only obtain the two-dimensional position of the UAV, so its altitude information cannot be obtained.…”

Section: Introductionmentioning

confidence: 99%

Data Fusion of UWB and IMU Based on Unscented Kalman Filter for Indoor Localization of Quadrotor UAV

et al. 2020

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The indoor location technique plays a essential role during the application of quadrotor unmanned aerial vehicle (UAV). However, the control design problem for the quadrotor UAV is quite difficult in the indoor environment due to the weak GPS signal. Based on Ultra Wide Band (UWB), the related positioning issues can be solved of UAV through base station with known coordinate position and equipment with location tag, but it is difficult to meet the high-precision operation requirements. In this paper, an indoor positioning design method combined with the Inertial Measurement Unit (IMU) and UWB positioning technology is proposed, which can effectively suppress the error accumulation of the IMU and further improve the positioning accuracy. Moreover, the system architecture for a class of quadrotor UAV is designed. The multisensor fusion technology based on unscented Kalman filter (UKF) is used to avoid neglecting the high-order terms of the nonlinear observation equations of UWB and IMU, which can effectively improve the accuracy of solving the nonlinear equations. Finally, a hardware-in-the-loop simulation platform is designed to verify the effectiveness of the indoor positioning method and improve the positioning accuracy.INDEX TERMS Ultra wide band (UWB), inertial measurement unit (IMU), data fusion, indoor localization, quadrotor UAV.

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Section: Introductionmentioning

confidence: 99%

Data Fusion of UWB and IMU Based on Unscented Kalman Filter for Indoor Localization of Quadrotor UAV

et al. 2020

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“…Consequently, point cloud matching is one of the fundamental elements of low-level perception. The iterative closest point (ICP) method is a well-known scan-matching and registration algorithm [29] that was proposed for point-to-point registration [30] and point-to-surface registration [31] in the 1990s to minimize the differences between two point clouds and to match them as closely as possible. This algorithm is robust and straightforward [32]; however, it has some problems in real-time applications such as SLAM due to heavy computation burden [33,34] and huge execution time [35].…”

Section: Introductionmentioning

confidence: 99%

High Definition 3D Map Creation Using GNSS/IMU/LiDAR Sensor Integration to Support Autonomous Vehicle Navigation

İlçi

Toth

2020

Sensors

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Recent developments in sensor technologies such as Global Navigation Satellite Systems (GNSS), Inertial Measurement Unit (IMU), Light Detection and Ranging (LiDAR), radar, and camera have led to emerging state-of-the-art autonomous systems, such as driverless vehicles or UAS (Unmanned Airborne Systems) swarms. These technologies necessitate the use of accurate object space information about the physical environment around the platform. This information can be generally provided by the suitable selection of the sensors, including sensor types and capabilities, the number of sensors, and their spatial arrangement. Since all these sensor technologies have different error sources and characteristics, rigorous sensor modeling is needed to eliminate/mitigate errors to obtain an accurate, reliable, and robust integrated solution. Mobile mapping systems are very similar to autonomous vehicles in terms of being able to reconstruct the environment around the platforms. However, they differ a lot in operations and objectives. Mobile mapping vehicles use professional grade sensors, such as geodetic grade GNSS, tactical grade IMU, mobile LiDAR, and metric cameras, and the solution is created in post-processing. In contrast, autonomous vehicles use simple/inexpensive sensors, require real-time operations, and are primarily interested in identifying and tracking moving objects. In this study, the main objective was to assess the performance potential of autonomous vehicle sensor systems to obtain high-definition maps based on only using Velodyne sensor data for creating accurate point clouds. In other words, no other sensor data were considered in this investigation. The results have confirmed that cm-level accuracy can be achieved.

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“…Other algorithms focuses on specific features from the scene to perform registration, such as corners or planes (Lamine Tazir et al 2018;Peng et al 2016). A well-known alternative is the iterative closest point registration, which has been widely used in the last years (Ren et al 2019;Kim et al 2018;Donoso et al 2017); therefore, many variations have been proposed, such as EM-ICP (Granger and Pennec 2002) and Generalized-ICP (Segal et al 2009). All these techniques can provide the pose of a vehicle when the LiDAR is mounted on it.…”

Section: Introductionmentioning

confidence: 99%

Analyzing and overcoming the effects of GNSS error on LiDAR based orchard parameters estimation

Guevara

Cheein

Gené-Mola

et al. 2020

Computers and Electronics in Agriculture

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Currently, 3D point clouds are obtained via LiDAR (Light Detection and Ranging) sensors to compute vegetation parameters to enhance agricultural operations. However, such a point cloud is intrinsically dependent on the GNSS (global navigation satellite system) antenna used to have absolute positioning of the sensor within the grove. Therefore, the error associated with the GNSS receiver is propagated to the LiDAR readings and, thus, to the crown or orchard parameters. In this work, we first describe the error propagation of GNSS over the laser scan measurements. Second, we present our proposal to overcome this effect based only on the LiDAR readings. Such a proposal uses a scan matching approach to reduce the error associated with the GNSS receiver. To accomplish such purpose, we fuse the information from the scan matching estimations with the GNSS measurements. In the experiments, we statistically analyze the dependence of the grove parameters

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Large-Scale Outdoor SLAM Based on 2D Lidar

Cited by 40 publications

References 31 publications

Data Fusion of UWB and IMU Based on Unscented Kalman Filter for Indoor Localization of Quadrotor UAV

Data Fusion of UWB and IMU Based on Unscented Kalman Filter for Indoor Localization of Quadrotor UAV

High Definition 3D Map Creation Using GNSS/IMU/LiDAR Sensor Integration to Support Autonomous Vehicle Navigation

Analyzing and overcoming the effects of GNSS error on LiDAR based orchard parameters estimation

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