Energy-Efficient Particle Filter SLAM for Autonomous Exploration

Abstract

Autonomous robots are increasingly used in more and more applications, such as warehouse robots, search-and-rescue robots and autonomous vacuum cleaners. These applications are often in environments where the GPS signals are denied or inaccurate, which makes it difficult to localize the robot in an unknown environment. To overcome this problem the framework of Simultaneous Localization and Mapping (SLAM) is typically used. This solution constructs a map of the environment with the use of cameras or range sensors, while keeping track of the location of the robot in it. To extend the exploration time of these battery powered robots, the energy consumption of the SLAM algorithm could be reduced. It is assumed that if the computational load of an algorithm reduces, the energy consumption of the algorithm reduces as well. An existing paradigm to solve SLAM is the use of a particle filter, which tracks the trajectory of the robot and simultaneously maps the environment. The question answered in this thesis is how to make this algorithm more energy-efficient to be able to deploy this framework in more applications and make the existing robots more sustainable. In this thesis two methods are investigated. In the first method, the information about the landmarks is incorporated in the trajectory estimation as spatial constraints, to try to achieve a higher accuracy with less particles and thus subsequently a smaller computational load. The proposed method is validated by simulations on synthetic datasets. This method shows improvements in terms of the estimation accuracy. However, it is more computational complex than the existing algorithms, so it is considered less energy-efficient. The second method researched in this thesis, is the implementation of a parallelized particle filter. This method processes the observation measurements in parallel for the different particles and communicates the information between the particles efficiently. It should reduce the computational time, to enable partial computation of the algorithm to reduce the computational load. This method is validated on the same datasets as the first method using simulations. This method shows improvements on the run time and thus on the computational load, especially for a larger number of particles and is therefore more energy-efficient. The two separate methods have been analyzed and compared with state-of-the-art methods. Both methods deliver equally good or better results in terms of accuracy. However, the computation time of constrained FastSLAM does not outweigh the improvement in accuracy. On the other hand, the parallelized particle filter shows significant improvement over the existing solutions.