Abstract
In this paper, we propose a novel algorithm to compute the initial structure of pose-graph based Simultaneous Localization and Mapping (SLAM) systems. We perform a Breadth-First Search (BFS) on the graph in order to obtain multiple votes regarding the location of a certain robot position from all of its previously processed neighbors. Next, we define the initial location of a pose as the average of the multiple alternatives. By adopting the proposed initialization approach, the number of iterations needed for optimization is significantly reduced while the computational complexity remains lightweight. We perform quantitative evaluation on various 2D and 3D benchmark datasets to demonstrate the advantages of the proposed method.
Highlights
The robustness of Simultaneous Localization and Mapping (SLAM) algorithms highly depends on the tracked key-features in consecutive frames and the graph optimization methods for concatenating intermittent key-frames along the trajectory
We used the following process to generate scenarios with different noise levels: for each dataset we used in our experiments, we added independent, Gaussian noise with 0 mean and (σ c, σ a) standard deviation to every measurement, and we examined how the Gauss-Newton algorithm performs from the different initial guesses
Since the runtime of MultiAncestor Spatial Approximation Tree (MASAT) is rather low, we examined the possibility of running MASAT before the Cauchy bootstrapping, providing an initial guess using the combination of the two methods
Summary
The robustness of Simultaneous Localization and Mapping (SLAM) algorithms highly depends on the tracked key-features in consecutive frames and the graph optimization methods for concatenating intermittent key-frames along the trajectory. A bad initial guess increases the computational time of the optimization and might lead the convergence of the algorithm to a local minimum, c.f., Figs. The initial guess is computed by heuristic methods either by using the odometry measurements or by using a minimum spanning tree search. Both methods are computationally lightweight and have low-complexity. In order to avoid the caveats of a bad initial guess, several high-complexity initialization algorithms were proposed, more recently Cauchy algorithm [8] All these algorithms are computationally complex and often include prior optimization steps, resulting in increased computational time. The best results in terms average normalized error, rate of successful convergence (robustness), and average number of iterations is achieved when the proposed algorithm is applied as a preprocessing step of the Cauchy algorithm [8]. We release the source code of the proposed algorithm, and provide all the data used to perform the comprehensive evaluation and comparison of the different approaches
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