Motion Estimation


Obstacle Avoidance
Motion Estimation
Local Mapping



Motion Estimation

One of the key issues in autonomous mobile robots is to keep track its position. Usually this problem is addressed by using the on board sensors to gather information of the environment for localization and mapping purposes. Many applications in robotics use techniques to estimate the robot displacement among successive range measurements. The objective of the scan matching techniques is to compute the relative motion of a vehicle between two consecutive configurations by maximizing the overlap between the range measurements obtained at each configuration. They usually assume an initial estimation of the relative pose of the scans that is provided by the vehicle odometry.

Our contribution resides in the definition of a new distance measure in the image space of the sensor that takes into account both, translation and rotation at the same time. The distance between two points is the norm (in a sense we are going to define) of the smallest rigid body transformation that leads a point to the other one. I.e our distance naturally depends on translation and rotation. We use this distance in both steps of the ICP algorithm:


  1. Matching of each point of a scan with the closest feature of the other scan in terms of our distance.

  2. Computation of relative displacement by least square minimization of the errors (in terms of our distance).

With this formulation we obtain results that ameliorate by far the algorithm that we were using (proposed by Lu and Millios in 1997) (the most used algorithm for scan matching) in terms of robustness and precision. Furthermore, we present in the paper the extension to the 3D problem, which could be used by the robotics, computer vision and graphics communities that use the ICP algorithm to address sensor motion estimation, location and map building, object recognition, pattern analysis, image registration, and scene understanding among others.

We show next some experiments conducted with a real vehicle:

Data integrated with the Odometry


Data corrected with the algorithm


Most relevant publications:

bulletJ. Minguez, F. Lamiraux and L. Montesano. Metric-Based Scan Matching Algorithms for Mobile Robot Displacement Estimation. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2005. Barcelona, Spain. (pdf)
bullet L. Montesano, J. Minguez and L. Montano. Probabilistic Scan Matching for Motion Estimation in Unstructured Environments.  Proceedings of the Conference on Intelligent Robots and Systems (IROS), 2005. Edmonton, Canada. (pdf)


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Principal | Obstacle Avoidance | Architectures | Motion Estimation | Local Mapping

Este sitio se actualizó por última vez el 28 de octubre de 2005