Neural network-based multiple robot simultaneous localization and mapping

Tomographic Feature-Based Map Merging for Multi-Robot Systems

Multi-robot systems require collective map information on surrounding environments to efficiently cooperate with one another on assigned tasks. This paper addresses the problem of grid map merging to obtain the collective map information in multi-robot systems with unknown initial poses. If inter-robot measurements are not available, the only way to merge the maps is to find and match the overlapping area between maps. This paper proposes a tomographic feature-based map merging method, which can be successfully conducted with relatively small overlapping areas. The first part of the proposed method is to estimate a map transformation matrix using the Radon transform which can extract tomographically salient features from individual grid maps. The second part is to determine the search space using Gaussian mixture models based on the estimated map transformation matrix. The final part is to optimize an objective function modeled from tomographic information within the determined search space. Evaluation results with various pairs of individual maps produced by simulations and experiments showed that the proposed method can merge the individual maps more accurately than other map merging methods.

Class of widely linear complex Kalman filters

Recently, a class of widely linear (augmented) complex-valued Kalman filters (KFs), that make use of augmented complex statistics, have been proposed for sequential state space estimation of the generality of complex signals. This was achieved in the context of neural network training, and has allowed for a unified treatment of both second-order circular and noncircular signals, that is, both those with rotation invariant and rotation-dependent distributions. In this paper, we revisit the augmented complex KF, augmented complex extended KF, and augmented complex unscented KF in a more general context, and analyze their performances for different degrees of noncircularity of input and the state and measurement noises. For rigor, a theoretical bound for the performance advantage of widely linear KFs over their strictly linear counterparts is provided. The analysis also addresses the duality with bivariate real-valued KFs, together with several issues of implementation. Simulations using both synthetic and real world proper and improper signals support the analysis.