Sensors and datasets¶
This page lists the possibilities to feed SLAM algorithms with sensory data in the MOLA framework.
1) Off-line: read from datasets¶
Parsing data from a dataset is the easiest way to test, debug, and benchmark any kind of localization, mapping, or classification algorithm. Existing options are:
From a ROS bag¶
Using mola-input-ros1 as a bridge between ROS->MOLA, then replaying the ROS bag using standard ROS tools, i.e. rosbag play.
From an MRPT rawlog¶
Using Module: mola-input-rawlog to parse a .rawlog file and have all its observations delivered to the rest of MOLA modules (sensor front-ends).
2) On-line: direct sensor connection¶
There exist two alternatives:
- to use ROS drivers for sensors and then use mola-input-ros1 as a bridge between ROS->MOLA.
- to directly connect to any sensor supported by mrpt-hwdrivers using the MOLA native Module: mola-input-hwdrivers.
In general, using mrpt-hwdrivers should be more efficient and convenient. A non-exhaustive list of sensors supported by this library is:
- 2D lidars: Hokuyo, SICK, RP-Lidar, Ibeo.
- 3D lidars: Velodyne.
- Cameras: Any camera supported by OpenCV and ffmpeg. Bumblebee2 stereo cameras.
- Depth sensors: Original XBox Kinect, any camera using OpenNI2.
- IMUs: xSens, KVH DSP3000.
- GNSS (GPS): Any sensor providing a standard NMEA interface. Novatel receivers.