With a leap in the era of communication and sensor technology, Automated Guided Vehicle (AGV) has found its major applicability in point-to-point smooth locomotion. For accomplishing precise autonomous intelligent navigation, there is a need of environment perception which is basically achieved from combination of multiple sensors rather than through an individual sensor. Proper sensor calibration paves the way to achieve collision-free navigation in a complex environmental structure filled with obstacles. The concerned research work proposes a novel working architecture where the concept of sensor fusion for collecting data is incorporated with geometrical representation of grid-based graphical path planning strategy executed by a two active wheeled robot platform. Data from 2D LiDAR (light detection and ranging) and 3D sensor (Intel Realsense D455) are fused together for acquiring the required depth data of the surrounding environment to achieve collision-free optimized path. Specifically, GPS (global positioning system) denied indoor environment has been considered here for carrying out the experimental procedure. The paper concludes with a comparative performance analysis of different procedures adapted to collect environmental data with respect to precise obstacle detection and also a comparative performance analysis of the Dijkstra algorithm, a significant graph theoretic path-finding approach accompanied by the respective data collection methods executed by an automated customized differential drive. The multisensory path navigation achieved 94.4% accuracy in obstacle prediction with 162 s execution time of algorithmic execution which outperforms other compared techniques.