# SLAM with cartesian point clouds

I'm doing an autonomous navigation project as my Capstone project with a Raspberry Pi 3 in c/c++ and I'm trying to figure out how to deal with the robot's locating. A robot has been given to me, it already receives user input to be controlled manually with an Arduino(receives characters from a RPI: 'f' = forward, 'r' = rotate etc). I will list the details for a bit of organization:

**Hardware:**

- Rpi3

- Arduino

- RPLiDAR A2(2D LiDAR)

- Servo(changes lidars pitch up and down to make 3D point clouds)

**With the code I've written so far in C++ from scratch I'm able to:**

- Convert spherical coordinates to xyz Point Cloud

- Discard points higher than robot and floor(not going to collide with those)

- Make a 2D map with the points (ignoring z-axis)

- Merge a new "map" to the main map using the robot's new position and yaw(I've only tested this manually, no IMU)

**Requirements:**

- Can't use a raw 2D data(RPLiDAR without change in pitch) to ensure it detects objects higher and lower than the LiDAR.

- D* Lite algoritm in C++ (not mine, so not too emotionally attached, the 2D map has been tested with it)

- GNUplot (just to confirm I'm processing the raw coordinates correctly)

After some research I've found out even with filters an IMU is not very reliable(at least by itself). I read yesterday about the SLAM algorithm and found out about the existence of ROS (wow...if I only knew beforehand...). I've been reading on how to use ROS and I've found a lot of different SLAM packages and also a package called robot_localization which takes odometry, pose and IMU messages.

I want to use the code I've written so far(make and merge 3D clouds and 2D maps) but I would like to use ROS to make things easier with the robot's locating and following the path returned by the D* algorithm(or maybe use a ROS pathfinding package?). Can I convert the 3D cloud(filtering floor and high points) to 2D map and use the SLAM algorithm? I'm still a little lost on what package is more practical(and light for the rpi3) for the data I have at hand, I'm totally new to ROS and would really appreciate to be pointed to an efficient approach to solve the locating and following the D* path.

Originally posted by liquidjorge on ROS Answers with karma: 1 on 2017-02-27

Post score: 0

I have limited experience with the ROS 2D SLAM packages, but hector slam can deal with changing pitch in the laser scans.

hector_mapping is a SLAM approach that can be used without odometry as well as on platforms that exhibit roll/pitch motion (of the sensor, the platform or both)

http://wiki.ros.org/hector_mapping

Originally posted by Felix Endres with karma: 6468 on 2017-02-28

This answer was ACCEPTED on the original site

Post score: 0