Open Access
Force Field Based n-Scan Alignment.
Rolf Lakämper,Nagesh Adluru,Longin Jan Latecki +2 more
- 01 Jan 2007
TL;DR: The presented algorithm solves the alignm ent problem utilizing a gradient descent approach motivated by physics, but exchanges laws of physics with constraints given by human perception.
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Abstract: We present a force field based approach for simultaneous alignment of multiple laser scans in robot mapping. It avoids sensitive behavior to wrong data associations and sparse sensing, which are the main challenges e.g. in multi r obot mapping under the constraints given in autonomous search an d rescue robotics. The presented algorithm solves the alignm ent problem utilizing a gradient descent approach motivated by physics, but exchanges laws of physics with constraints giv en by human perception. Experiments on different real world data sets show the successful application of the algorithm.
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Citations
Using virtual scans for improved mapping and evaluation
Rolf Lakaemper,Nagesh Adluru +1 more
TL;DR: A system to enhance the performance of feature correspondence based alignment algorithms for laser scan data that augments the sensor data with hypotheses about ideal models of objects in the robot’s environment and replaces the estimated ‘Virtual Scans’ with ground truth maps.
•Proceedings Article
FaMSA: Fast multi-scan alignment with partially known correspondences
Ernesto Homar Teniente Avilés,Juan Andrade-Cetto +1 more
- 01 Jan 2011
TL;DR: FaMSA allows to quickly match a new scan with multiple consecutive scans at a time, with the consequent benefits in computational speed, and is shown to be comparable to that of independent scan alignment.
Improving sparse laser scan alignment with Virtual Scans
Rolf Lakaemper,Nagesh Adluru +1 more
- 14 Oct 2008
TL;DR: A system to increase the performance of feature correspondence based alignment algorithms for laser scan data by augmenting the sensor data with hypotheses (dasiaVirtual Scanspsila) about ideal models of these objects by analyzing the current aligned map estimated by the underlying iterative alignment algorithm.
4
References
A method for registration of 3-D shapes
Paul J. Besl,H.D. McKay +1 more
TL;DR: In this paper, the authors describe a general-purpose representation-independent method for the accurate and computationally efficient registration of 3D shapes including free-form curves and surfaces, based on the iterative closest point (ICP) algorithm, which requires only a procedure to find the closest point on a geometric entity to a given point.
20.6K
A fast algorithm for particle simulations
TL;DR: An algorithm is presented for the rapid evaluation of the potential and force fields in systems involving large numbers of particles whose interactions are Coulombic or gravitational in nature, making it considerably more practical for large-scale problems encountered in plasma physics, fluid dynamics, molecular dynamics, and celestial mechanics.
5.5K
Efficient variants of the ICP algorithm
Szymon Rusinkiewicz,Marc Levoy +1 more
- 01 May 2001
TL;DR: An implementation is demonstrated that is able to align two range images in a few tens of milliseconds, assuming a good initial guess, and has potential application to real-time 3D model acquisition and model-based tracking.
Snakes, shapes, and gradient vector flow
Chenyang Xu,Jerry L. Prince +1 more
TL;DR: This paper presents a new external force for active contours, which is computed as a diffusion of the gradient vectors of a gray-level or binary edge map derived from the image, and has a large capture range and is able to move snakes into boundary concavities.
Object modeling by registration of multiple range images
YangQuan Chen,Gerard Medioni +1 more
- 09 Apr 1991
TL;DR: The authors propose an approach that works on range data directly and registers successive views with enough overlapping area to get an accurate transformation between views and performs a functional that does not require point-to-point matches.
3.4K