Simplifying the process of mapping and modeling with low cost sensors

During his internship at Willow Garage, Alex Ichim, from EPFL Switzerland concentrated his efforts on simplifying the process of using off-the-shelf RGB-D cameras to capture objects and rooms in 3D. In contrast to other proposed systems utilizing low cost sensors, his goal was to leverage the geometric information gathered from the depth camera as much as possible, without the need for RGB cameras to align elements together in space. The result is comparable to more complex state-of-the-art SLAM algorithms that use color features.

To help with the captre of 3D information, Alex and his team present a system that makes use of geometric features such as planar regions. Planes are used for different purposes ranging from noise removal, alignment of pairs of frames, and global error relaxation within the captured information. In addition, much of their effort was spent with enhancing the different stages of point cloud registration by implementing and benchmarking techniques such as filtering, normal computation, correspondence estimation, and filtering. 

At the end, his team refined how the collection of 3D data can be transformed into a compressed representation, such as colorized 3D models. Such a system opens a lot of possibilities given the simplicity of the setup, ranging from scanning small objects such as toys, larger items such as cars, going all the way to reconstructing entire rooms. Once captured, the models can be converted into physical form using off-the-shelf 3D printers.

A thorough evaluation of possible RGB extensions of the application are left for future work. In the meantime, a complete analysis of the components of the system, as well as implementations are available online at www.pointclouds.org.

For more information about Alex's work, check out his thesis (PDF) and presentation (PDF).