Informative path planning algorithms are of paramount importance in applications like disaster management to efficiently gather information through a priori unknown environments. This is, however, a complex problem that involves finding a globally optimal path that gathers the maximum amount of information (e.g., the largest map with a minimum travelling distance) while using partial and uncertain local measurements. This paper addresses this problem by proposing a novel heuristic algorithm that continuously estimates the potential mapping gain for different sub-areas across the partially created map, and then uses these estimations to locally navigate the robot. Furthermore, this paper presents a novel algorithm to calculate a benchmark solution, where the map is a priori known to the planar, to evaluate the efficacy of the developed heuristic algorithm over different test scenarios. The findings indicate that the efficiency of the proposed algorithm, measured in terms of the mapped area per unit of travelling distance, ranges from 70% to 80% of the benchmark solution in various test scenarios. In essence, the algorithm demonstrates the capability to generate paths that come close to the globally optimal path provided by the benchmark solution.