Probabilistic graphical fusion of LiDAR, GPS, and 3D building maps for urban UAV navigation

Chen, Derek; Gao, Grace Xingxin

doi:10.1002/navi.298

Cited by 41 publications

(15 citation statements)

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“…The recently proposed FGO formulation (Indelman et al., 2013) has opened a new avenue for multisensor integration (Chen & Gao, 2019; Pfeifer & Protzel, 2019a, 2019b). It is represented as a probabilistic graphical model containing various nodes associated with the system states and factors representing the measurements.…”

Section: Related Workmentioning

confidence: 99%

Factor graph optimization for GNSS/INS integration: A comparison with the extended Kalman filter

et al. 2021

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Factor graph optimization (FGO) recently has attracted attention as an alternative to the extended Kalman filter (EKF) for GNSS‐INS integration. This study evaluates both loosely and tightly coupled integrations of GNSS code pseudorange and INS measurements for real‐time positioning, using both conventional EKF and FGO with a dataset collected in an urban canyon in Hong Kong. The FGO strength is analyzed by degenerating the FGO‐based estimator into an “EKF‐like estimator.” In addition, the effects of window size on FGO performance are evaluated by considering both the GNSS pseudorange error models and environmental conditions. We conclude that the conventional FGO outperforms the EKF because of the following two factors: (1) FGO uses multiple iterations during the estimation to achieve a robust estimation; and (2) FGO better explores the time correlation between the measurements and states, based on a batch of historical data, when the measurements do not follow the Gaussian noise assumption.

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Section: Related Workmentioning

confidence: 99%

Factor graph optimization for GNSS/INS integration: A comparison with the extended Kalman filter

et al. 2021

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“…Researches related to multi-sensor fusion on cars have been discussed in Bresson et al (2017). Several researchers (Lamon et al , 2006; Wan et al , 2018; Chen and Gao, 2019) fuse the data from multiple sensors by means of probabilistic state estimation and pose optimization techniques. However, the strategy of simultaneously running pose optimization algorithm and acquiring GPS poses for data filtering all the time consumes lots of computing resources.…”

Section: Related Workmentioning

confidence: 99%

“…We find that the two algorithms enable the GLO-SLAM system to be robust and performant in the experiments. Different from some other methods which simultaneously obtain two kinds of poses all the time and fuse them in a probabilistic framework (Lamon et al , 2006; Wan et al , 2018; Chen and Gao, 2019), the strategy in our GLO-SLAM uses either poses from GPS or poses from EG-LOAM for mapping. The GLO-SLAM only runs the LiDAR odometry in local areas where no reliable GPS data can be obtained.…”

Section: Introductionmentioning

confidence: 99%

GLO-SLAM: a slam system optimally combining GPS and LiDAR odometry

Lin

Zhang

2021

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Purpose Large-scale and precise three-dimensional (3D) map play an important role in autonomous driving and robot positioning. However, it is difficult to get accurate poses for mapping. On one hand, the global positioning system (GPS) data are not always reliable owing to multipath effect and poor satellite visibility in many urban environments. In another hand, the LiDAR-based odometry has accumulative errors. This paper aims to propose a novel simultaneous localization and mapping (SLAM) system to obtain large-scale and precise 3D map. Design/methodology/approach The proposed SLAM system optimally integrates the GPS data and a LiDAR odometry. In this system, two core algorithms are developed. To effectively verify reliability of the GPS data, VGL (the abbreviation of Verify GPS data with LiDAR data) algorithm is proposed and the points from LiDAR are used by the algorithm. To obtain accurate poses in GPS-denied areas, this paper proposes EG-LOAM algorithm, a LiDAR odometry with local optimization strategy to eliminate the accumulative errors by means of reliable GPS data. Findings On the KITTI data set and the customized outdoor data set, the system is able to generate high-precision 3D map in both GPS-denied areas and areas covered by GPS. Meanwhile, the VGL algorithm is proved to be able to verify reliability of the GPS data with confidence and the EG-LOAM outperform the state-of-the-art baselines. Originality/value A novel SLAM system is proposed to obtain large-scale and precise 3D map. To improve the robustness of the system, the VGL algorithm and the EG-LOAM are designed. The whole system as well as the two algorithms have a satisfactory performance in experiments.

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“…Different UAVs are made of numerous kind of light fused materials in order to lighter the weight and expand the rate of mobility while flying, can be additionally installed with other needful equipment's, including camera, GPS (global positioning system), GPS guided rockets, navigation, sensors (Chen & Gao, 2019). The UAVs The different type of UAVs are available in the market, it depends upon cost, an expansive assortment of shapes, sizes, capacities, and functions.…”

Section: Literature Reviewmentioning

confidence: 99%