The ability to build cognitive maps of unknown environments in a continuous, unsupervised manner is an important capability for autonomous agents. Deep-reinforcement neural networks, despite demonstrating impressive capabilities across diverse domains, fail to rival mammalian proficiency in this critical navigation task due to their lack of explainability, sample inefficiency, and limited capacity to generalize to new environments. This paper presents a Modular and Incremental Network with Enhanced Representation and Vertical Abstraction (MINERVA), a bioinspired and explainable architecture for sensorimotor map learning that aims to extend the capabilities of growing neural architectures by incorporating principles observed in mammalian spatial cognition, including distributed and hierarchical processing of inputs and sparse coding mechanisms. The algorithm is compared with the Temporospatial Merge Grow When Required (TMGWR) network, which was previously demonstrated in a maze navigation context to be superior to algorithms such as growing neural gas (GNG), Grow When Required (GWR) and time GNG (TGNG) in terms of disambiguation performance, sensorial representation accuracy, and sensorimotor-link error. From the experiments conducted, MINERVA demonstrated more robust performance in these metrics with better multi-sensorial processing capabilities, which can be leveraged in solving more complex challenges in more difficult sensorial environments.