An IMU/Sonar-based Extended Kalman Filter for Mini-UAV Localization in Indoor Environment

Shu, Xiangqian; Yang, Lingyu; Feng, Xiao; Zhang, Jing

doi:10.1109/gncc42960.2018.9018955

Cited by 5 publications

(1 citation statement)

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“…Adaptation to low-light conditions can be made using onboard headlights, which however give limited imaging range [1]. Sonars or ultrasonic sensors can be used for localization [4,35] and as proximity sensors for obstacle detection [16]. Ultra-wideband (UWB) positioning was demonstrated based on the principle of range measurement from one onboard tag to eight external anchors [36].…”

Section: Related Workmentioning

confidence: 99%

Lidar Positioning for Indoor Precision Navigation

Holmberg

Karlsson

Tulldahl

2022

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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Lidar based simultaneous localization and mapping methods can be adapted for deployment on small autonomous vehicles operating in unmapped indoor environments. For this purpose, we propose a method which combines inertial data, low-drift lidar odometry, planar primitives, and loop closing in a graph-based structure. The accuracy of our method is experimentally evaluated, using a high-resolution lidar, and compared to the state-ofthe-art methods LIO-SAM and Cartographer. We specifically address the lateral positioning accuracy when passing through narrow openings, where high accuracy is a prerequisite for safe operation of autonomous vehicles. The test cases include doorways, slightly wider reference passages, and a larger corridor environment. We observe a reduced lateral accuracy for all three methods when passing through the narrow openings compared to operation in larger spaces. Compared to state-of-the-art, our method shows better results in the narrow passages, and comparable results in the other environments with reasonably low usage of CPU and memory resources.

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