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loam_continuous

Package Summary

Documented

Laser Odometry and Mapping (continuous spin version)

loam_continuous

Package Summary

Documented

Laser Odometry and Mapping (continuous spin version)

Package Summary

Documented

Laser Odometry and Mapping (continuous spin version)

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maps.jpg

Overview

Laser Odometry and Mapping (Loam) is a realtime method for state estimation and mapping using a 3D lidar. The program contains two major threads running parallel. An "odometry" thread computes motion of the lidar between two sweeps, at a higher frame rate. It also removes distortion in the point cloud caused by motion of the lidar. A "mapping" thread takes the undistorted point cloud and incrementally builds a map, while simultaneously computes pose of the lidar on the map at a lower frame rate. The lidar state estimation is combination of the outputs from the two threads.

If an IMU is available, the orientation (integrated from angular rate) and acceleration measurements are used to deal with general motion of the lidar, while the program takes care of linear motion.

The program is tested on a laptop with 2.5 GHz quad cores and 6 Gib memory (the program consumes two cores). It uses the lidar on an UAV that spins continuously (see following figure). Another version of the program using back and forth spin is available here.

lidar2.jpg

Usage

To run the program, users need to download the code from GitHub, or follow the link on the top of this page. The program can be started by ROS launch file (available in the downloaded folder), which starts four nodes and rivz:

roslaunch loam_continuous.launch

Datasets are available for download from here, or at the bottom of this page. Please make sure the data files are for continuous spin (not back and forth spin). With the program running (from the launch file), users can play the data file:

rosbag play data_file_name.bag

Note that if a slow computer is used, users can try to play the data file at a low speed, e.g. play the data file at half speed:

rosbag play data_file_name.bag -r 0.5

Datasets

Robot city bridge(Video): mapping a bridge with an UAV

robot_city_bridge.jpg

References

J. Zhang and S. Singh. LOAM: Lidar Odometry and Mapping in Real-time. Robotics: Science and Systems Conference (RSS). Berkeley, CA, July 2014.

More Info

YouTube channel

Wiki: loam_continuous (last edited 2014-05-15 00:20:41 by jizhang-cmu)