The Rosario Dataset
The Rosario Dataset is an agricultural dataset collected on-board out weed removing robot. The dataset is composed by six different sequences in a soybean field and it contains stereo images, IMU measurements, wheel odometry and GPS-RTK (position ground-truth).
http://www.cifasis-conicet.gov.ar/robot
Dense S-PTAM
Dense S-PTAM reconstructs a dense map of the environment in real-time using the disparity maps and the pose estimated by the original S-PTAM SLAM system.
https://github.com/CIFASIS/dense-sptam
DS-PTAM
DS-PTAM is a distributed architecture for the S-PTAM stereo SLAM system. This architecture is developed on the ROS framework, separating the localization and mapping tasks into two independent ROS nodes. The DS-PTAM system is ideal for mobile robots with low computing power because it allows to run the localization module on-board and the mapping module-which has a higher computational cost-on a remote base station, relieving the load on the on-board processor.
https://github.com/CIFASIS/distributed-sptam
S-PTAM
S-PTAM is a Stereo SLAM system able to compute the camera trajectory in real-time. It heavily exploits the parallel nature of the SLAM problem, separating the time-constrained pose estimation from less pressing matters such as map building and refinement tasks. On the other hand, the stereo setting allows to reconstruct a metric 3D map for each frame of stereo images, improving the accuracy of the mapping process with respect to monocular SLAM and avoiding the well-known bootstrapping problem. Also, the real scale of the environment is an essential feature for robots which have to interact with their surrounding workspace.