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Abstract | ||
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In this paper, we propose a new algorithm, named MCSLAM (Multiple Constrained SLAM ), designed to dynamically adapt each optimization to the variable number of parameters families (sensor pose, roughly known objects poses and dimensions, delay between sensors, ...) and heterogeneous constraints (reprojection error, distance from points or edges to object surface, acceleration...). The proposed algorithm is based on three contributions: 1) a new Levenberg-Marquardt C++ library named LMA and freely available 1 2) an architecture allowing a high level of flexibility and performances 3) a real-time usage of a temporal spline curve as the parametric trajectory model that provides an efficient way to add heterogeneous constraints within the optimization. The main idea of LMA is to provide a simple interface with a nonintrusive mechanism of adaptation to a problem while maintaining good performances. LMA works as a meta-program using C++ template to analyse at compile-time (CT) the problem to optimize from a list of C++ functors. The parameters are deduced from the functors arguments and the degree of freedom (dof ) of each parameter is defined by the user. Then LMA generates a data structure to store the functors and the parameters in tuple according to the number of parameters families and constraints. The resolution of the normal equations is written efficiently using a sparse representation constructed with a set of small matrices of static sizes. The LMA library solves the normal equations using dense Cholesky or a sparse PCG specially designed to manage little matrices of static size. It also implements the classical optimization tricks to be effective on little, medium, and big size problems. LMA also implements common features as automatic differentiation and robust cost functions. The continuous-time representation of the trajectory is used to deal with constraints on the motion. This allows to mix data from many unsynchronised sensors and evolution model. To apply constraints on the 3D structure of the environment, 3D models of coarsely knowns shapes are used. To represent the motion, we use the uniform cumulative b-spline described by Lovegrove et al. [2] but we separate position and orientation in two different splines and we use the Rodriguez formula to compute the exponential and the logarithm of SO3 group. Moreover, we adapt the keyframe based SLAM to deal with the spline: key-frames are constrained to be on the spline. We also use every inter-key-frame poses computed by the localization process to apply a weak constraint on the spline. A temporal sliding window of 3 seconds is used to select, from the SLAM and the IMU, the more recent data used to constrain the spline. The MCSLAM algorithm is based on a graph composed by constraints, parameters and dependencies. First, the problem configuration is analysed at compile-time to generate a specified LMA solver, whose cost function to minimize is the sum of the constraints C dynamically |
Year | Venue | Field |
|---|---|---|
2015 | BMVC | Spline (mathematics),Data structure,Computer vision,Tuple,Computer science,Sparse approximation,Automatic differentiation,Algorithm,Parametric statistics,Artificial intelligence,Solver,Cholesky decomposition |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
26 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Datta Ramadasan | 1 | 0 | 1.69 |
| Marc Chevaldonné | 2 | 1 | 1.37 |
| Thierry Chateau | 3 | 13 | 1.40 |