In this paper, we propose a new construction of generalized root protograph (GRP) low-density parity-check (LDPC) codes which has a guaranteed diversity order. New hybrid-automatic request with incremental redundancy (H-ARQ-IR) is also proposed to ensure high diversity and rate-compatibility in wireless communication systems, where the GRP LDPC codes are employed along with a method called feedback encoding. The proposed H-ARQ-IR has advantages of low-complexity and near-optimal performance. In order to show its performance behavior, theoretical analysis for the proposed scheme is conducted for the block fading channel. Furthermore, numerical analysis shows that it achieves near maximum diversity order under the belief propagation (BP) decoding with binary phase shift keying (BPSK) modulation and high-order quadrature amplitude modulations (QAMs).

We analyze the symbol measures for iterative erasure insertion and decoding of a Reed-Solomon coded SFH/MFSK system over jamming channels. In contrast to conventional erasure insertion schemes, iterative schemes do not require any preoptimized threshold or channel state information at the receiver. We confirm the performance improvement using a generalized minimum distance (GMD) decoding method with three different symbol measures. To analyze performance, we propose a new analysis framework considering the “trapped-error” probability. From analysis and the simulation results, we show that ratio-based GMD decoding has the best performance among the one-dimensional iterative erasure insertion and decoding schemes.

This paper addresses the limitation of the conventional Rao-Blackwellized unscented particle filters. The problem is on the usage of the overconfident optimal proposal distribution caused by perfect map assumption, so that predictive robot poses are sampled from the underestimated error covariance in the particle filtering process. The proposed solution computes more precise error covariance of the robot which contains uncertainties of the robot, map, and measurement noise. Experimental results using the benchmark dataset confirmed that the covariance of the proposed method is always larger than that of the conventional method while inducing slower increasing rate of the weight variance with less resamplings.

In this paper, we present a robust RBPF-SLAM algorithm for mobile robots in non-static environments. We propose an approach for sampling particles from multiple ancestor sets, not from just one prior set. This sampling method increases the robustness of SLAM algorithm, because some particles can be updated by only observations consistent with the map, even if observation at certain time step is corrupted by environmental changes. Corrupted observations are filtered out from recursive Bayesian update process by the proposed sampling method. We also present an intermediate path estimation method to use abandoned sensor information reflected from relocated objects for map update. The map can represent the changed configuration of non-static environment by the stored sensor information and the estimated path. Results of simulations and experiments in non-static environments show the robustness of proposed RBPF-SLAM algorithm using sonar sensors.

In this paper, we address the problem of building a grid map using cheap sonar sensors, that is, the problem of using erroneous sensors when seeking to model an environment as accurately as possible. We rely on the inconsistency of information among sonar measurements and the sound pressure of the waves from the sonar sensors to develop a new method of detecting incorrect sonar readings, called the conflict evaluation with sound pressure (CEsp). To fuse the correct measurements into a map, we start with the maximum likelihood approach due to its ability to manage the angular uncertainty of sonar sensors. However, since this approach suffers from heavy computational complexity, we convert it to a light logic problem called the maximum approximated likelihood (MAL) approach. Integrating the MAL approach with the CEsp method results in the conflict-evaluated maximum approximated likelihood (CEMAL) approach. The CEMAL approach generates a very accurate map that is close to the map that would be built by accurate laser sensors, and does not require adjustment of parameters for various environments.

The Rao-Blackwellized particle filtering SLAM (RBPF-SLAM) still suffers from a large-loop or nested-loop closure due to a lack of hypotheses. This makes the filter optimistic and results in the RBPF to be a short term solution. This paper provides an important insight to the current resampling scheme and proposes an approach to delaying the resampling decision until the loop closing. A discussion about the issue of preserving the importance weight is also presented with developing the weight-smoothing method. This approach is a necessary step for the RBPF-SLAM, but to the best of our knowledge, no literature deals with this concept. Since the delayed resampling maintains the possible hypotheses, the filter consistency is significantly improved. Moreover since the resampling takes the loop closure into account, the required number of particles can be proportional to the loop size of the environment. The computational burden, caused by the resampling process, is also drastically reduced due to the delaying decision. Experimental results in large-scale environment with large and nested loops illustrate the effectiveness of our approach.

The Rao--Blackwellized particle filter (RBPF) and FastSLAM have two important limitations, which are the derivation of the Jacobian matrices and the linear approximations of nonlinear functions. These can make the filter inconsistent. Another challenge is to reduce the number of particles while maintaining the estimation accuracy. This paper provides a robust new algorithm based on the scaled unscented transformation called unscented FastSLAM (UFastSLAM). It overcomes the important drawbacks of the previous frameworks by directly using nonlinear relations. This approach improves the filter consistency and state estimation accuracy, and requires smaller number of particles than the FastSLAM approach. Simulation results in large-scale environments and experimental results with a benchmark dataset are presented, demonstrating the superiority of the UFastSLAM algorithm.

Rao-Blackwellized Particle Filter and FastSLAM have become popular tools to solve the Simultaneous Localization and Mapping (SLAM) problem. However, the above techniques have two important potential limitations, which are the derivation of the Jacobian matrices and the linear approximations to the nonlinear functions. Also, one of the major challenges of both Rao-Blackwellized particle filter and FastSLAM is to reduce the number of particles while maintaining the estimation accuracy. This paper proposes a new algorithm based on unscented transformation called Unscented FastSLAM (UFastSLAM) that overcomes important drawbacks of the Rao-Blackwellized particle filter frameworks by directly using nonlinear relations. Experimental results in large scale environments are presented that demonstrate the effectiveness of the UFastSLAM algorithm over the previous approaches.