7 accepted papers
Robots operating in human environments are often required to recognise, grasp and manipulate objects. Identifying the locations of objects amongst their complex surroundings is therefore an important capability. However, when environments are unstructured and cluttered, as is typical for indoor huma…
In this article, we present and evaluate a system, which allows a mobile robot to autonomously detect, model, and re-recognize objects in everyday environments. While other systems have demonstrated one of these elements, to our knowledge, we present the first system, which is capable of doing all o
This paper presents an algorithm for surfel color and position enhancement from RGB-D data acquired across multiple image frames. Surfel-based reconstruction algorithms associate each RGB-D frame pixel to a surfel in the model. As the reconstruction progresses, surfel color and position are the aver…
Cheap RGB-D sensors are ubiquitous in robotics. They typically contain a consumer-grade color camera that suffers from significant optical nonlinearities, often referred to as vignetting effects. For example, in Asus Xtion Live Pro cameras the pixels in the corners are two times darker than those in…
We present an end-to-end method for active object classification in cluttered scenes from RGB-D data. Our algorithms predict the quality of future viewpoints in the form of entropy using both class and pose. Occlusions are explicitly modeled in predicting the visible regions of objects, which modula
We present a novel method based on saliency and segmentation to generate generic object candidates from RGB-D data. Our method uses saliency as a cue to roughly estimate the location and extent of the objects present in the scene. Salient regions are used to glue together the segments obtained from…
We propose a method for recognizing rigid object instances in RGB-D point clouds by accumulating low-level information from keypoint correspondences over multiple observations. Compared to existing multi-view approaches, we make fewer assumptions on the recognition problem, dealing with cluttered an…