Difference between revisions of "OpenSource"

From IntRoLab
 
(25 intermediate revisions by the same user not shown)
Line 1: Line 1:
 
= Active Projects =
 
= Active Projects =
 +
* [[OpenECoSys]]
 +
* [[RTAB-Map]]
 +
* [[ManyEars]]
  
== ManyEars ==
+
== RTAB-Map ==
  
=== Related IntRoLab Project(s) ===
+
<center>
* [[AUDIBLE]]
+
<code>{{#ev:youtube|CAk-QGMlQmI}}</code>
* [[UltimateRobot]]
+
</center>
  
== OpenECoSys ==
+
[http://code.google.com/p/rtabmap/ RTAB-Map] is a memory management approach for real-time appearance-based loop closure detection for autonomous robot (using a simple camera or webcam). The loop closure detection approach is fully incremental, starting with an empty memory. The robot builds his own representation of the environment by linking new acquired images with previous ones (e.g., detecting loop closures). The method implemented here can be considered as a Topological SLAM (Simultaneous Localization And Mapping) approach. Memory management makes it possible to process each new image under a fixed real-time limit, thus ideal for long-term operation.
  
 
=== Related IntRoLab Project(s) ===
 
=== Related IntRoLab Project(s) ===
* [[AZIMUT]]
+
* [[RTAB-Map]]
* [[UltimateRobot]]
 
* [[Teletrauma]]
 
* [[Telerobot]]
 
* [[DEA]]
 
* [[DDRA]]
 
 
 
= Old Projects =
 
 
 
== MARIE ==
 
  
== FlowDesigner / RobotFlow ==
+
= Older Projects =

Latest revision as of 18:40, 15 April 2011

Active Projects

RTAB-Map

RTAB-Map is a memory management approach for real-time appearance-based loop closure detection for autonomous robot (using a simple camera or webcam). The loop closure detection approach is fully incremental, starting with an empty memory. The robot builds his own representation of the environment by linking new acquired images with previous ones (e.g., detecting loop closures). The method implemented here can be considered as a Topological SLAM (Simultaneous Localization And Mapping) approach. Memory management makes it possible to process each new image under a fixed real-time limit, thus ideal for long-term operation.

Related IntRoLab Project(s)

Older Projects