Graph-based segmentation for colored 3D laser point clouds

Strom, Johannes; Richardson, Andrew J.; Olson, Edwin

doi:10.1109/iros.2010.5650459

Cited by 138 publications

(90 citation statements)

References 14 publications

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“…To avoid per-point normal estimation, Enjarini et al [12] designed the gradient of depth feature for plane segmentation, which could be rapidly computed. Graph-based segmentation using self-adaptive threshold was also used [13], [14] (0.17s per 148,500 points [13]). Although our method also uses a graph to represent data relation, our method differs from the previous methods as follows: 1) no RGB information is used; 2) no per-point normal estimation is required; and more importantly, 3) dynamic edge weights are used instead of static ones which fix the merging order as in [13].…”

Section: B Related Workmentioning

confidence: 99%

Fast plane extraction in organized point clouds using agglomerative hierarchical clustering

Feng

Taguchi

Kamat

2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

192

188

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Abstract-Real-time plane extraction in 3D point clouds is crucial to many robotics applications. We present a novel algorithm for reliably detecting multiple planes in real time in organized point clouds obtained from devices such as Kinect sensors. By uniformly dividing such a point cloud into nonoverlapping groups of points in the image space, we first construct a graph whose node and edge represent a group of points and their neighborhood respectively. We then perform an agglomerative hierarchical clustering on this graph to systematically merge nodes belonging to the same plane until the plane fitting mean squared error exceeds a threshold. Finally we refine the extracted planes using pixel-wise region growing. Our experiments demonstrate that the proposed algorithm can reliably detect all major planes in the scene at a frame rate of more than 35Hz for 640×480 point clouds, which to the best of our knowledge is much faster than state-of-the-art algorithms.

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Section: B Related Workmentioning

confidence: 99%

Fast plane extraction in organized point clouds using agglomerative hierarchical clustering

Feng

Taguchi

Kamat

2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

192

188

View full text Add to dashboard Cite

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“…Deschaud and Goulette use a region growing approach made robust to noise by growing in a voxel space over the input data rather than the raw points themselves [13]. Some algorithms extend 2D graph cut theory towards 3D point cloud data [14,15,16]. These algorithms are designed for general object segmentation and their complexity is in general too high for plane detection, unlike the low-complexity algorithm proposed by Rabbani et al [17], which imposes a smoothness constraint on segmentation (discussed later in Section 4).…”

Section: Related Workmentioning

confidence: 99%

Incremental and batch planar simplification of dense point cloud maps

Whelan

Bondarev

et al. 2015

Robotics and Autonomous Systems

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Dense RGB-D SLAM techniques and high-fidelity LIDAR scanners are examples from an abundant set of systems capable of providing multi-million point datasets. These datasets quickly become difficult to process due to the sheer volume of data, typically containing significant redundant information, such as the representation of planar surfaces with millions of points. In order to exploit the richness of information provided by dense methods in real-time robotics, techniques are required to reduce the inherent redundancy of the data. In this paper we present a method for incremental planar segmentation of a gradually expanding point cloud map and a method for efficient triangulation and texturing of planar surface segments. Experimental results show that our incremental segmentation method is capable of running in real-time while producing a segmentation faithful to what would be achieved using a batch segmentation method. Our results also show that the proposed planar simplification and triangulation algorithm removes more than 90% of the input planar points, leading to a triangulation with only 10% of the original quantity of triangles per planar segment. Additionally, our texture generation algorithm preserves all colour information contained within planar segments, resulting in a visually appealing and geometrically accurate simplified representation.

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“…If 3D-data is available, 3D features such as surface normals or curvature might additionally be exploited [10,11]. However, visual or spatial boundaries need not always correspond to object boundaries [12,13], so not all ambiguities can be resolved [12,[14][15][16]].…”

Section: A Non-interactive Visual Segmentationmentioning

confidence: 99%

Probabilistic Segmentation and Targeted Exploration of Objects in Cluttered Environments

2014

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Abstract-Creating robots that can act autonomously in dynamic, unstructured environments requires dealing with novel objects. Thus, an off-line learning phase is not sufficient for recognizing and manipulating such objects. Rather, an autonomous robot needs to acquire knowledge through its own interaction with its environment, without using heuristics encoding human insights about the domain. Interaction also allows information that is not present in static images of a scene to be elicited. Out of a potentially large set of possible interactions, a robot must select actions that are expected to have the most informative outcomes to learn efficiently. In the proposed bottom-up, probabilistic approach, the robot achieves this goal by quantifying the expected informativeness of its own actions in information-theoretic terms. We use this approach to segment a scene into its constituent objects. We retain a probability distribution over segmentations. We show that this approach is robust in the presence of noise and uncertainty in real-world experiments. Evaluations show that the proposed information-theoretic approach allows a robot to efficiently determine the composite structure of its environment. We also show that our probabilistic model allows straightforward integration of multiple modalities, such as movement data and static scene features. Learned static scene features allow for experience from similar environments to speed up learning for new scenes.

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Graph-based segmentation for colored 3D laser point clouds

Cited by 138 publications

References 14 publications

Fast plane extraction in organized point clouds using agglomerative hierarchical clustering

Fast plane extraction in organized point clouds using agglomerative hierarchical clustering

Incremental and batch planar simplification of dense point cloud maps

Probabilistic Segmentation and Targeted Exploration of Objects in Cluttered Environments

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