# rf2o_laser_odometry_b

**Repository Path**: batjack/rf2o_laser_odometry2

## Basic Information

- **Project Name**: rf2o_laser_odometry_b
- **Description**: 在ubuntu18.04上使用ROS-melodic时对rf2o_laser_odometry的支持不够好（该功能包主要是针对ROS-indigo版本开发，在ROS-kinetic支持尚可）
因为该功能包使用了MRPT库，而在melodic中MRPT有MRPT及MRPT2两个版本，与前期ROS版本中的MRPT有差异，尽管简单的修改可以使rf2o功能包编译成功，但实际使用时效果很差，经反复查找，该作者对该功能包进行了修改，在melodic中使用效果很好！！
原帖地址：https://github.com/MAPIRlab/rf2o_laser_odometry/issues/17；
原github地址：https://github.com/tianb03/rf2o_laser_odometry
- **Primary Language**: Unknown
- **License**: GPL-3.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 1
- **Created**: 2021-03-10
- **Last Updated**: 2025-08-14

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# rf2o_laser_odometry
Estimation of 2D odometry based on planar laser scans. Useful for mobile robots with innacurate base odometry. 

RF2O is a fast and precise method to estimate the planar motion of a lidar from consecutive range scans. For every scanned point we formulate the range flow constraint equation in terms of the sensor velocity, and minimize a robust function of the resulting geometric constraints to obtain the motion estimate. Conversely to traditional approaches, this method does not search for correspondences but performs dense scan alignment based on the scan gradients, in the fashion of dense 3D visual odometry. 

Its very low computational cost (0.9 milliseconds on a single CPU core) together whit its high precission, makes RF2O a suitable method for those robotic applications that require planar odometry.

For full description of the algorithm, please refer to: **Planar Odometry from a Radial Laser Scanner. A Range Flow-based Approach. ICRA 2016** Available at: http://mapir.isa.uma.es/work/rf2o