3D Mapping with Semantic Knowledge

Nüchter, Andreas; Wulf, Oliver; Lingemann, Kai; Hertzberg, Joachim; Wagner, Bernd; Surmann, Hartmut

doi:10.1007/11780519_30

Cited by 59 publications

(52 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This means that each point p i represents the distance on the three defined coordinate axes from the acquisition viewpoint to the surface that the point has been sampled on. Though there are many ways of measuring distances and converting them to 3D points, in the context of mobile robotic applications [Nue09], the two most used approaches are:…”

Section: Data Acquisitionmentioning

confidence: 99%

Semantic 3D Object Maps for Everyday Manipulation in Human Living Environments

2010

View full text Add to dashboard Cite

ENVIRONMENT models serve as important resources for an autonomous robot by providing it with the necessary task-relevant information about its habitat. Their use enables robots to perform their tasks more reliably, flexibly, and efficiently. As autonomous robotic platforms get more sophisticated manipulation capabilities, they also need more expressive and comprehensive environment models: for manipulation purposes their models have to include the objects present in the world, together with their position, form, and other aspects, as well as an interpretation of these objects with respect to the robot tasks.This thesis proposes Semantic 3D Object Models as a novel representation of the robot's operating environment that satisfies these requirements and shows how these models can be automatically acquired from dense 3D range data. The thesis contributes in two important ways to the research area acquisition of environment models.The first contribution is a novel framework for Semantic 3D Object Model acquisition from Point Cloud Data. The functionality of this framework includes robust alignment and integration mechanisms for partial data views, fast segmentation into regions based on local surface characteristics, and reliable object detection, categorization, and reconstruction. The computed models are semantic in that they infer structures in the data that are meaningful with respect to the robot task. Examples of such objects are doors and handles, supporting planes, cupboards, walls, or movable smaller objects. The second key contribution is point cloud representations based on 3D point feature histograms (3D-PFHs), which model the local surface geometry for each point. 3D-PFHs distinguish themselves from alternative 3D feature representations in that they are very fast to compute, robust against variations in pose and sampling density, and cope well with noisy sensor data. Their use substantially improves the quality of the Semantic 3D Object Models acquired, as well as the speed with which they are computed. 3D-PFHs come with specific software tools that allow for the learning of surface characteristics based on their underlying geometry, the assembly of most distinctive 3D points from a given cloud, as well as limited view-invariant correspondence search for 3D registration. IIIThe contributions presented in this thesis have been fully implemented and empirically evaluated on different robots performing different tasks in different environments. The first demonstration relates to the problem of cleaning tables by disposing the objects on them into a garbage bin with a personal robotic assistant in the presence of humans in its working space. The framework for Semantic 3D Object Model acquisition is demonstrated and used to construct dynamic 3D collision maps, annotate the surrounding world with semantic labels, and extract object clusters supported by tables in real-time performance. The second demonstration presents an on-the-fly model acquisition system for door and handle identification from n...

show abstract

Section: Data Acquisitionmentioning

confidence: 99%

Semantic 3D Object Maps for Everyday Manipulation in Human Living Environments

2010

View full text Add to dashboard Cite

show abstract

“…The main advantage of using 3-D models of environments is that they are more descriptive and have richer information content than 2-D models in terms of the features extracted from the environments, resulting in less ambiguity in distinguishing features [4]. 3-D models are used in fields varying from robot motion planning and navigation [3,5,6,7], art and architecture [8,9,10,11,12] to industry/urban planning, water management, and forestry documentation [13,14,15]. 3-D models can be obtained using a variety of sensors measuring range or intensity.…”

Section: -D Modeling Techniques Allow Describing Environments Includmentioning

confidence: 99%

Novel Compression Algorithm Based on Sparse Sampling of 3-D Laser Range Scans

Dobrucali

Barshan

2012

The Computer Journal

View full text Add to dashboard Cite

3-D models of environments can be very useful and are commonly employed in areas such as robotics, art and architecture, environmental planning and documentation. A 3-D model is typically comprised of a large number of measurements. When 3-D models of environments need to be transmitted or stored, they should be compressed efficiently to use the capacity of the communication channel or the storage medium effectively. In this thesis, we propose a novel compression technique based on compressive sampling, applied to sparse representations of 3-D laser range measurements. The main issue here is finding highly sparse representations of the range measurements, since they do not have such representations in common domains, such as the frequency domain. To solve this problem, we develop a new algorithm to generate sparse innovations between consecutive range measurements acquired while the sensor moves. We compare the sparsity of our innovations with others generated by estimation and filtering. Furthermore, we compare the compression performance of our lossy compression method with widely used lossless and lossy compression techniques.The proposed method offers small compression ratio and provides a reasonable compromise between reconstruction error and processing time.

show abstract

“…Such classifiers have been used previously Mozos et al (2005) to classify indoor places. Later, several approaches were proposed in literature to improve performance by taking advantage of object recognition (Nuchter et al, 2005) and probabilistic environment models (Mozos et al, 2007). A GHMM consists of a discrete-time and discrete-space Markov process that contains some hidden parameters and emits observable outputs (Rabiner, 1989).…”

Section: State-of-art Algorithmsmentioning

confidence: 99%

Stochastic automata for outdoor semantic mapping using optimised signal quantisation

Caponetti

Blas

Blanke

2011

Control Engineering Practice

View full text Add to dashboard Cite

Autonomous robots require many types of information to obtain intelligent and safe behaviours. For outdoor operations, semantic mapping is essential and this paper proposes a stochastic automaton to localise the robot within the semantic map. For correct modelling and classification under uncertainty, this paper suggests quantising robotic perceptual features, according to a probabilistic description, and then optimising the quantisation. The proposed method is compared with other state-of-the-art techniques that can assess the confidence of their classification. Data recorded on an autonomous agricultural robot are used for verification and the new method is shown to compare very favourably with existing ones.

show abstract

3D Mapping with Semantic Knowledge

Cited by 59 publications

References 14 publications

Semantic 3D Object Maps for Everyday Manipulation in Human Living Environments

Semantic 3D Object Maps for Everyday Manipulation in Human Living Environments

Novel Compression Algorithm Based on Sparse Sampling of 3-D Laser Range Scans

Stochastic automata for outdoor semantic mapping using optimised signal quantisation

Contact Info

Product

Resources

About