Active SLAM: A Review on Last Decade.

This article presents a comprehensive review of the Active Simultaneous Localization and Mapping (A-SLAM) research conducted over the past decade. It explores the formulation, applications, and methodologies employed in A-SLAM, particularly in trajectory generation and control-action selection, drawing on concepts from Information Theory (IT) and the Theory of Optimal Experimental Design (TOED). This review includes both qualitative and quantitative analyses of various approaches, deployment scenarios, configurations, path-planning methods, and utility functions within A-SLAM research. Furthermore, this article introduces a novel analysis of Active Collaborative SLAM (AC-SLAM), focusing on collaborative aspects within SLAM systems. It includes a thorough examination of collaborative parameters and approaches, supported by both qualitative and statistical assessments. This study also identifies limitations in the existing literature and suggests potential avenues for future research. This survey serves as a valuable resource for researchers seeking insights into A-SLAM methods and techniques, offering a current overview of A-SLAM formulation.

Data fusion fault tolerant strategy for a quadrotor UAV under sensors and software faults.

This article presents the design and implementation of a fault tolerant architecture for sensor fusion that tolerates faults on a quadrotor unmanned aerial vehicle (UAV). It aims to tolerate both hardware sensors faults (GPS jamming, IMU lock or freezing, magnetometer sensitivity to high power magnetic fields...) and software faults (faults in the Kalman filter, bad parameters initialization....). The proposed architecture uses data fusion with Kalman filters in order to estimate the states (position and orientation) of the UAV. It includes an analytical redundancy using the dynamic model of the system. The estimations of the defined Kalman filters and the dynamic model feed a weighted average voter, which increases the accuracy of the outputs and the error detection process. The proposed architecture allows multiple recovery solutions to a faulty system and thus increasing its flexibility. The architecture is validated using numerical simulations and experimental flights in real outdoor environment using a quadrotor.

Relative Pose Based Redundancy Removal: Collaborative RGB-D Data Transmission in Mobile Visual Sensor Networks.

In this paper, the Relative Pose based Redundancy Removal (RPRR) scheme is presented, which has been designed for mobile RGB-D sensor networks operating under bandwidth-constrained operational scenarios. The scheme considers a multiview scenario in which pairs of sensors observe the same scene from different viewpoints, and detect the redundant visual and depth information to prevent their transmission leading to a significant improvement in wireless channel usage efficiency and power savings. We envisage applications in which the environment is static, and rapid 3D mapping of an enclosed area of interest is required, such as disaster recovery and support operations after earthquakes or industrial accidents. Experimental results show that wireless channel utilization is improved by 250% and battery consumption is halved when the RPRR scheme is used instead of sending the sensor images independently.