Contour-based next-best view planning from point cloud segmentation of unknown objects

Monica, Riccardo; Aleotti, Jacopo

doi:10.1007/s10514-017-9618-0

Cited by 48 publications

(23 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Broadly, active perception is defined as the situation where a robot "adopts strategies for decisions of sensor placement or sensor configuration" in order to perform a task [12]. It is a concept which has been applied to a wide variety of robotic tasks, such as mapping [13,14], object modelling [15], object identification [16,17] and path planning [18]. Common strategies for active perception focus on planning the expected next best action to efficiently minimise measurement uncertainty or maximise information gain via a metric such as Shannon entropy [19] or KL divergence [20].…”

Section: Related Workmentioning

confidence: 99%

Multi-View Picking: Next-best-view Reaching for Improved Grasping in Clutter

Morrison

Corke

Leitner

2019

2019 International Conference on Robotics and Automation (ICRA)

View full text Add to dashboard Cite

Camera viewpoint selection is an important aspect of visual grasp detection, especially in clutter where many occlusions are present. Where other approaches use a static camera position or fixed data collection routines, our Multi-View Picking (MVP) controller uses an active perception approach to choose informative viewpoints based directly on a distribution of grasp pose estimates in real time, reducing uncertainty in the grasp poses caused by clutter and occlusions. In trials of grasping 20 objects from clutter, our MVP controller achieves 80% grasp success, outperforming a single-viewpoint grasp detector by 12%. We also show that our approach is both more accurate and more efficient than approaches which consider multiple fixed viewpoints. Code is available at https://github.com/dougsm/mvp_grasp

show abstract

Section: Related Workmentioning

confidence: 99%

Multi-View Picking: Next-best-view Reaching for Improved Grasping in Clutter

Morrison

Corke

Leitner

2019

2019 International Conference on Robotics and Automation (ICRA)

View full text Add to dashboard Cite

show abstract

“…10 The reader can find a thorough review of view planning methods in the following surveys. 1,9,14,20 Some methods go beyond the NBV calculation and determine the robot state that matches the sensor pose Torabi and Gupta, 10 combines inverse kinematics with a probabilistic road map (PRM), Kriegel et al, 2 uses an RRT to get possible paths, and Monica and Aleotti 21 uses optimal motion planning implemented in the MoveIt! ROS stack.…”

Section: Related Workmentioning

confidence: 99%

Tree-based search of the next best view/state for three-dimensional object reconstruction

Vasquez-Gomez

Sucar

Murrieta-Cid

et al. 2018

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

Three-dimensional models from real objects have many applications in robotics. To automatically build a three-dimensional model from an object, it is essential to determine where to place the range sensor in order to completely observe the object. However, the view (position and orientation) of the sensor is not sufficient, given that its corresponding robot state needs to be calculated. Additionally, a collision-free trajectory to reach that state is required. In this article, we directly find the state of the robot whose corresponding sensor view observes the object. This method does not require to calculate the inverse kinematics of the robot. Unlike previous approaches, the proposed method guides the search with a tree structure based on a rapidly exploring random tree overcoming previous sampling techniques. In addition, we propose an information metric that improves the reconstruction performance of previous information metrics.

show abstract

“…With this information, mean-shift clustering is applied and the cluster with the highest value is chosen as the next best view. Other approaches use contours to calculate the unseen parts [7]. Vasquez-Gomez et al [13] used a two stage system that improves the quality of the modeling by predicting a nextbest-view and evaluating a set of neighbor views, eventually selecting the best among all of them.…”

Section: Related Workmentioning

confidence: 99%

Best Viewpoint Tracking for Camera Mounted on Robotic Arm with Dynamic Obstacles

Tyleček¹

2017

2017 International Conference on 3D Vision (3DV)

View full text Add to dashboard Cite

The problem of finding a next best viewpoint for 3D modeling or scene mapping has been explored in computer vision over the last decade. This paper tackles a similar problem, but with different characteristics. It proposes a method for dynamic next best viewpoint recovery of a target point while avoiding possible occlusions. Since the environment can change, the method has to iteratively find the next best view with a global understanding of the free and occupied parts.We model the problem as a set of possible viewpoints which correspond to the centers of the facets of a virtual tessellated hemisphere covering the scene. Taking into account occlusions, distances between current and future viewpoints, quality of the viewpoint and joint constraints (robot arm joint distances or limits), we evaluate the next best viewpoint. The proposal has been evaluated on 8 different scenarios with different occlusions and a short 3D video sequence to validate its dynamic performance.

show abstract

Contour-based next-best view planning from point cloud segmentation of unknown objects

Cited by 48 publications

References 39 publications

Multi-View Picking: Next-best-view Reaching for Improved Grasping in Clutter

Multi-View Picking: Next-best-view Reaching for Improved Grasping in Clutter

Tree-based search of the next best view/state for three-dimensional object reconstruction

Best Viewpoint Tracking for Camera Mounted on Robotic Arm with Dynamic Obstacles

Contact Info

Product

Resources

About