Multi-contour initial pose estimation for 3D registration

Reliable manipulation of everyday household objects is essential to the success of service robots. In order to accurately manipulate these objects, robots need to know objects’ full 6-DOF pose, which is challenging due to sensor noise, clutters and occlusions. In this paper, we present a new approach for effectively guessing the object pose given an observation of just a small patch of the object, by leveraging the fact that many household objects can only keep stable on a planar surface under a small set of poses. In particular, for each stable pose of an object, we slice the object with horizontal planes and extract multiple cross-section contours. The pose estimation is then reduced to find a stable pose whose contour matches best with that of the sensor data, and this can be solved efficiently by convolution. Experiments on the manipulation tasks in the DARPA Robotics Challenge validate our approach. In addition, we also investigate our method’s performance on object recognition tasks raising in the challenge.postprin

Accelerating kd-tree Searches for all k-nearest Neighbours

Finding the k nearest neighbours of each point in a point cloud forms an integral part of many point-cloud processing tasks. One common approach is to build a kd-tree over the points and then iteratively query the k nearest neighbors of each point. We introduce a simple modification to these queries to exploit the coherence between successive points; no changes are required to the kd-tree data structure. The path from the root to the appropriate leaf is updated incrementally, and backtracking is done bottom-up. We show that this can reduce the time to compute the neighbourhood graph of a 3D point cloud by over 10%, and by up to 24% when k = 1. The gains scale with the depth of the kd-tree, and the method is suitable for parallel implementation

Estimation of the precision of a structured light system in oil paintings on canvas

The conservation and authentication of pictorial artworks is considered an important part of the preservation of the cultural heritage. The use of non-destructive testing allows the obtaining of accurate information about the state of pictorial artworks, without direct contact between the equipment used and the sample. In particular, the use of this kind of technology is recommended in obtaining three-dimensional surface digital models, as it provides high-resolution information that constitutes a kind of fingerprint of the samples. In the case of pictorial artworks with some kind of surface relief, one of the most useful technologies is structured light (SL). In this paper the minimum difference in height that can be distinguished with this technology is estimated, establishing experimentally both the error committed in the measurement process and the precision in the use of this technology. The study, focused on the case of oil paintings on canvas, has been developed using a low-cost system to ensure its wide use

6D SLAM with GPGPU computation

Abstract: The main goal was to improve a state of the art 6D SLAM algorithm with a new GPGPU-based implementation of data registration module. Data registration is based on ICP (Iterative Closest Point) algorithm that is fully implemented in the GPU with NVIDIA FERMI architecture. In our research we focus on mobile robot inspection intervention systems applicable in hazardous environments. The goal is to deliver a complete system capable of being used in real life. In this paper we demonstrate our achievements in the field of on line robot localization and mapping. We demonstrated an experiment in real large environment. We compared two strategies of data alignment -simple ICP and ICP using so called meta scan

A real time visual SLAM for RGB-D cameras based on chamfer distance and occupancy grid

International audienceWe present a feature based visual SLAM method that uses chamfer distance to estimate the camera motion from RGB-D images. The proposed method does not require any matching which is an expensive operation and always generates false matching that affects the estimated camera motion. Our approach registers the input image iteratively by minimizing the distance between the feature points and the occupancy grid using a distance map. We demonstrate with real experiments the capability of the method to build accurate 3D map of the environment with a hand-held camera. While the system was mainly developed to work with RGB-D camera, occupancy grid representation gives the method the ability to work with various types of sensors, we show the capacity of the system to construct accurate 2D maps using telemeter data. We also discuss the similarities between the proposed approach and the traditional ICP algorithm