Kalman Filter and Complementary Filter in Attitude Estimation of APM

Lin, Feng; Miao, Xi Lan; Qu, Xiao Guang

doi:10.4028/www.scientific.net/amm.577.794

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…However, there is a high chance that Kalman filter-based linear state estimation will diverge under high vibration, as in the case of crane application. The complementary filter [26] is used for orientation estimation using an IMU and exhibits better estimation accuracy and robustness than the Kalman filter under high vibration [27], [28], [29], [30]. Combined with the structural information of a crane, the complementary filter can be used for sensor pose estimation.…”

Section: Lidar-imu Fusionmentioning

confidence: 99%

“…Considering the challenges mentioned above, we use a slowly-rotating 2D lidar and an IMU as base components and attach them to the crane boom. As the crane boom moves arbitrarily during operations, we estimate the lidar pose for each scan using the IMU using a complementary filter [26] with moving average filtering, which is more robust to severe vibration than Kalman filter-based methods [27], [28], [29], [30]. To further improve the map accuracy, we develop a new pose graph optimization method using planar environmental constraints that naturally exist in construction sites.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

3D Mapping for a Large Crane Using Rotating 2D-Lidar and IMU Attached to the Crane Boom

2023

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This paper describes a novel method for large-scale 3D mapping for construction cranes with an arbitrary motion of the sensor system (2D lidar and IMU) attached to the crane boom. A heavy lidar with a slowly rotating base is needed to make a large-scale map both vertically and horizontally for cranes. This sensor configuration and mapping conditions entail handling each 2D scan separately, making it difficult to adopt existing rotating 2D lidar-based methods that construct a virtual 3D scan from a set of 2D scans. In the proposed method, we introduce a complementary filter with moving average filtering for lidar pose estimation, which is more robust to severe vibration than Kalman filter-based methods. As there are only a small amount of overlaps between 2D lidar scans, we propose a map correction method based on a pose graph optimization with planar environmental constraints. We evaluate the proposed method in a simulation and a small-sized real environment and compare it with one of the state-of-the-art methods. The evaluation results reveal that the proposed method can accurately estimate the sensor poses, thereby generating a high-quality, large-scale 3D point cloud map.

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