In robotic teleoperation, the knowledge of the state of the remote environment in real time is paramount. Advances in the development of highly accurate 3D cameras able to provide high-quality point clouds appear to be a feasible solution for generating live, up-to-date virtual environments. Unfortunately, the exceptional accuracy and high density of these data represent a burden for communications requiring a large bandwidth affecting setups where the local and remote systems are particularly geographically distant. This paper presents a multiple level-of-detail (LoD) compression strategy for 3D data based on tree-like codification structures capable of compressing a single data frame at multiple resolutions using dynamically configured parameters. The level of compression (resolution) of objects is prioritised based on: (i) placement on the scene; and (ii) the type of object. For the former, classical point cloud fitting and segmentation techniques are implemented; for the latter, user-defined prioritisation is considered. The results obtained are compared using a single LoD (whole-scene) compression technique previously proposed by the authors. Results showed a considerable improvement to the transmitted data size and updated frame rate while maintaining low distortion after decompression.

Rigid transformation is a popular method to estimate the robot motion given two sets of corresponding points seen from two different locations. Range imaging sensors, such as stereo camera and structured-light 3-D scanner, can be used to provide these corresponding points, however, such sensors have measurement uncertainty defined by intervals with upper and lower bounds. This paper presents a new approach to use interval analysis and simultaneously estimate the following: (1) the robot motion and, (2) the position of the landmarks with respect to the initial frame of reference, i.e. The robot initial pose. We will show that using this approach, the uncertainties of the landmark positions decrease over time, which causes the uncertainty of the robot pose to remain bounded. Our approach is illustrated with examples using simulated data, and real data acquired by Kinect sensor.

This article presents a novel systematic methodology for the detection of interest points in 3D point clouds and its corresponding descriptors by using the information of an RGB camera and a structured-light sensor. This is achieved by fusing Speeded-Up Robust Features (SURF) in the image space, and histograms that statistically represent the relationship of three dimensional geometric data around the interest points. The SURF algorithm is implemented over an image whose pixel coordinates have a direct corresponding 3D point, thus allowing the fusion of both approaches. By combining both methodologies, it is intent to define a set of interest points whose descriptors are able to maintain the intrinsic characteristics of its constituent parts such as repeatability, distinctiveness and robustness while remaining compact and fast to compute. The detected points will be use for both, localization and mapping of mobile robots in partially unknown environments.