NDLI: Low-drift and real-time lidar odometry and mapping

Content Provider	Springer Nature Link
Author	Zhang, Ji Singh, Sanjiv
Copyright Year	2016
Abstract	Here we propose a real-time method for low-drift odometry and mapping using range measurements from a 3D laser scanner moving in 6-DOF. The problem is hard because the range measurements are received at different times, and errors in motion estimation (especially without an external reference such as GPS) cause mis-registration of the resulting point cloud. To date, coherent 3D maps have been built by off-line batch methods, often using loop closure to correct for drift over time. Our method achieves both low-drift in motion estimation and low-computational complexity. The key idea that makes this level of performance possible is the division of the complex problem of Simultaneous Localization and Mapping, which seeks to optimize a large number of variables simultaneously, into two algorithms. One algorithm performs odometry at a high-frequency but at low fidelity to estimate velocity of the laser scanner. Although not necessary, if an IMU is available, it can provide a motion prior and mitigate for gross, high-frequency motion. A second algorithm runs at an order of magnitude lower frequency for fine matching and registration of the point cloud. Combination of the two algorithms allows map creation in real-time. Our method has been evaluated by indoor and outdoor experiments as well as the KITTI odometry benchmark. The results indicate that the proposed method can achieve accuracy comparable to the state of the art offline, batch methods.
Starting Page	401
Ending Page	416
Page Count	16
File Format	PDF
ISSN	09295593
Journal	Autonomous Robots
Volume Number	41
Issue Number	2
e-ISSN	15737527
Language	English
Publisher	Springer US
Publisher Date	2016-02-18
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Ego-motion estimation Mapping Continuous-time Lidar Robotics and Automation Artificial Intelligence (incl. Robotics) Computer Imaging, Vision, Pattern Recognition and Graphics Control, Robotics, Mechatronics
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence

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