An Approach for Representing Maps in SLAM Based on Grid Maps and Sparse Matrices

• Elizabeth Morales-Muñoz UCSP
• Claudia Cervantes-Jilaja UCSP
• Dennis Barrios-Aranibar UCSP
• Raquel Patiño-Escarcina UCSP

Resumo

Graphs and matrices are widely used to represent maps in solutions for Simultaneous Localization and Mapping (SLAM). Space representation is crucial for map creation, finding the shortest path, planning a trajectory or route, tracking landmarks, etc. The choice of the data structure can present disadvantages such as more memory consumption when using matrices, or, the need of a transforming function to represent a map when graphs (as structure) are applied as structure. This paper propose a representing map using a storing structure that allows dynamic growth and optimization of memory consumption in SLAM. The so-called MPTE-SLAM structure, proposed here, suggests the usage of a sparse matrix of occupancy matrices to represent a map for SLAM. The goal of this approach is to reduce the use of memory (if compared with traditional metric maps also) also having a compact representation suitable for path planning and other tasks for autonomous robots; without the need of a transforming function (if compared with graph based solutions that return a map). This approach allows the addition and updating of values of the sub-matrices quickly and with dynamic growth on the overall map within the structure in the same way graphs permit; also it is suitable for a real-time implementation of solutions for SLAM. For implementation and testing, a non-holonomic mobile robot in an indoor environment was used. The final results showed that the MPTE-SLAM structure uses less memory than metric maps when optimal sub-matrices sizes are applied. Through experimentation an optimal size for submatrices (10x10) was determined; this size yielded a memory consumption less than 4.3 GB in all test experiments; also, it was observed that this approach also has dynamic growth in the structure plus the no-loss of its metric nature which gives it an advantage over graph-based representations.

Palavras-chave: Simultaneous localization and mapping, Sparse matrices, Memory management, Robot kinematics