Robust estimation of planar surfaces using spatio-temporal RANSAC for applications in autonomous vehicle navigation

Mufti, Faisal; Mahony, Robert; Heinzmann, J.

doi:10.1016/j.robot.2011.08.009

Cited by 29 publications

(12 citation statements)

References 61 publications

(69 reference statements)

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“…Similar to us, Mufti et al [MMH12] introduce a spatio‐temporal RANSAC algorithm for dynamic scan processing. Nevertheless, their setup consists of a moving ToF camera scanning a 3D outdoor scene and their goal is the detection of the planar (static) ground.…”

Section: Related Workmentioning

confidence: 99%

Mobility Fitting using 4D RANSAC

Wan

et al. 2016

Computer Graphics Forum

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Capturing the dynamics of articulated models is becoming increasingly important. Dynamics, better than geometry, encode the functional information of articulated objects such as humans, robots and mechanics. Acquired dynamic data is noisy, sparse, and temporarily incoherent. The latter property is especially prominent for analysis of dynamics. Thus, processing scanned dynamic data is typically an ill‐posed problem. We present an algorithm that robustly computes the joints representing the dynamics of a scanned articulated object. Our key idea is to by‐pass the reconstruction of the underlying surface geometry and directly solve for motion joints. To cope with the often‐times extremely incoherent scans, we propose a space‐time fitting‐and‐voting approach in the spirit of RANSAC. We assume a restricted set of articulated motions defined by a set of joints which we fit to the 4D dynamic data and measure their fitting quality. Thus, we repeatedly select random subsets and fit with joints, searching for an optimal candidate set of mobility parameters. Without having to reconstruct surfaces as intermediate means, our approach gains the advantage of being robust and efficient. Results demonstrate the ability to reconstruct dynamics of various articulated objects consisting of a wide range of complex and compound motions.

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Section: Related Workmentioning

confidence: 99%

Mobility Fitting using 4D RANSAC

Wan

et al. 2016

Computer Graphics Forum

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“…Matulic et al [8] developed an ubiquitous projection method to create an immersive interactive environment using RANSAC to extract planar surfaces. Mufti et al [9] used time information to estimate planar surfaces and overcome the low resolution and the measurement errors of infrared time-of-flight cameras for autonomous vehicle navigation coupled with RANSAC to find the planes.…”

Section: Detection Of Planar Surfacesmentioning

confidence: 99%

Projection Surfaces Detection and Image Correction for Mobile Robots in HRI

et al. 2017

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Projectors have become a widespread tool to share information in Human-Robot Interaction with large groups of people in a comfortable way. Finding a suitable vertical surface becomes a problem when the projector changes positions when a mobile robot is looking for suitable surfaces to project. Two problems must be addressed to achieve a correct undistorted image: (i) finding the biggest suitable surface free from obstacles and (ii) adapting the output image to correct the distortion due to the angle between the robot and a nonorthogonal surface. We propose a RANSAC-based method that detects a vertical plane inside a point cloud. Then, inside this plane, we apply a rectangle-fitting algorithm over the region in which the projector can work. Finally, the algorithm checks the surface looking for imperfections and occlusions and transforms the original image using a homography matrix to display it over the area detected. The proposed solution can detect projection areas in real-time using a single Kinect camera, which makes it suitable for applications where a robot interacts with other people in unknown environments. Our Projection Surfaces Detector and the Image Correction module allow a mobile robot to find the right surface and display images without deformation, improving its ability to interact with people.

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“…Oniga et al [21] utilized a random sample consensus (RANSAC) algorithm to detect a road surface and cluster obstacles based on the density of the sensed points, and Mufti et al [22] presented a spatio-temporal RANSAC framework to detect planar surfaces. Based on the planar features of the ground, the detected area was then segmented.…”

Section: Related Workmentioning

confidence: 99%

Intuitive Terrain Reconstruction Using Height Observation-Based Ground Segmentation and 3D Object Boundary Estimation

Song

Cho

et al. 2012

Sensors

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Mobile robot operators must make rapid decisions based on information about the robot’s surrounding environment. This means that terrain modeling and photorealistic visualization are required for the remote operation of mobile robots. We have produced a voxel map and textured mesh from the 2D and 3D datasets collected by a robot’s array of sensors, but some upper parts of objects are beyond the sensors’ measurements and these parts are missing in the terrain reconstruction result. This result is an incomplete terrain model. To solve this problem, we present a new ground segmentation method to detect non-ground data in the reconstructed voxel map. Our method uses height histograms to estimate the ground height range, and a Gibbs-Markov random field model to refine the segmentation results. To reconstruct a complete terrain model of the 3D environment, we develop a 3D boundary estimation method for non-ground objects. We apply a boundary detection technique to the 2D image, before estimating and refining the actual height values of the non-ground vertices in the reconstructed textured mesh. Our proposed methods were tested in an outdoor environment in which trees and buildings were not completely sensed. Our results show that the time required for ground segmentation is faster than that for data sensing, which is necessary for a real-time approach. In addition, those parts of objects that were not sensed are accurately recovered to retrieve their real-world appearances.

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Robust estimation of planar surfaces using spatio-temporal RANSAC for applications in autonomous vehicle navigation

Cited by 29 publications

References 61 publications

Mobility Fitting using 4D RANSAC

Mobility Fitting using 4D RANSAC

Projection Surfaces Detection and Image Correction for Mobile Robots in HRI

Intuitive Terrain Reconstruction Using Height Observation-Based Ground Segmentation and 3D Object Boundary Estimation

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