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HANDHELD LIDAR ODOMETRY ESTIMATION AND MAPPING SYSTEM
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
2018 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Ego-motion sensors are commonly used for pose estimation in Simultaneous Localization And Mapping (SLAM) algorithms. Inertial Measurement Units (IMUs) are popular sensors but suffer from integration drift over longer time scales. To remedy the drift they are often used in combination with additional sensors, such as a LiDAR. Pose estimation is used when scans, produced by these additional sensors, are being matched. The matching of scans can be computationally heavy as one scan can contain millions of data points. Methods exist to simplify the problem of finding the relative pose between sensor data, such as the Normal Distribution Transform SLAM algorithm. The algorithm separates the point cloud data into a voxelgrid and represent each voxel as a normal distribution, effectively decreasing the amount of data points. Registration is based on a function which converges to a minimum. Sub-optimal conditions can cause the function to converge at a local minimum. To remedy this problem this thesis explores the benefits of combining IMU sensor data to estimate the pose to be used in the NDT SLAM algorithm.

Place, publisher, year, edition, pages
2018. , p. 24
Keywords [en]
SLAM, NDT, Normal Distribution Transform, Robotics, IMU, LiDAR, GPS, RTK-GPS, ego-motion, pose estimation
National Category
Embedded Systems Robotics Signal Processing
Identifiers
URN: urn:nbn:se:mdh:diva-41137OAI: oai:DiVA.org:mdh-41137DiVA, id: diva2:1254001
Subject / course
Computer Science
Available from: 2019-09-18 Created: 2018-10-08 Last updated: 2019-09-18Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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