Algorithmes SLAM : Vers une implémentation embarquée

Autonomous navigation is a main axis of research in the field of mobile robotics. In this context, the robot must have an algorithm that allow the robot to move autonomously in a complex and unfamiliar environments. Mapping in advance by a human operator is a tedious and time consuming task. On the other hand, it is not always reliable, especially when the structure of the environment changes. SLAM algorithms allow a robot to map its environment while localizing it in the space.SLAM algorithms are becoming more efficient, but there is no full hardware or architectural implementation that has taken place . Such implantation of architecture must take into account the energy consumption, the embeddability and computing power. This scientific work aims to evaluate the embedded systems implementing locatization and scene reconstruction (SLAM). The methodology will adopt an approach AAM ( Algorithm Architecture Matching) to improve the efficiency of the implementation of algorithms especially for systems with high constaints. SLAM embedded system must have an electronic and software architecture to ensure the production of relevant data from sensor information, while ensuring the localization of the robot in its environment. Therefore, the objective is to define, for a chosen algorithm, an architecture model that meets the constraints of embedded systems. The first work of this thesis was to explore the different algorithmic approaches for solving the SLAM problem. Further study of these algorithms is performed. This allows us to evaluate four different kinds of algorithms: FastSLAM2.0, ORB SLAM, SLAM RatSLAM and linear. These algorithms were then evaluated on multiple architectures for embedded systems to study their portability on energy low consumption systems and limited resources. The comparison takes into account the time of execution and consistency of results. After having deeply analyzed the temporal evaluations for each algorithm, the FastSLAM2.0 was finally chosen for its compromise performance-consistency of localization result and execution time, as a candidate for further study on an embedded heterogeneous architecture. The second part of this thesis is devoted to the study of an embedded implementing of the monocular FastSLAM2.0 which is dedicated to large scale environments. An algorithmic modification of the FastSLAM2.0 was necessary in order to better adapt it to the constraints imposed by the largescale environments. The resulting system is designed around a parallel multi-core architecture. Using an algorithm architecture matching approach, the FastSLAM2.0 was implemeted on a heterogeneous CPU-GPU architecture. Uisng an effective algorithme partitioning, an overall acceleration factor o about 22 was obtained on a recent dedicated architecture for embedded systems. The nature of the execution of FastSLAM2.0 algorithm could benefit from a highly parallel architecture. A second instance hardware based on programmable FPGA architecture is proposed. The implantation was performed using high-level synthesis tools to reduce development time. A comparison of the results of implementation on the hardware architecture compared to GPU-based architectures was realized. The gains obtained are promising, even compared to a high-end GPU that currently have a large number of cores. The resulting system can map a large environments while maintainingthe balance between the consistency of the localization results and real time performance. Using multiple calculators involves the use of a means of data exchange between them. This requires strong coupling (communication bus and shared memory). This thesis work has put forward the interests of parallel heterogeneous architectures (multicore, GPU) for embedding the SLAM algorithms. The FPGA-based heterogeneous architectures can particularly become potential candidatesto bring complex algorithms dealing with massive data.