  Simultaneous localization and mapping (SLAM) is used to predict the dynamic
motion path of a moving platform based on the location coordinates and the
precise mapping of the physical environment. SLAM has great potential in
augmented reality (AR), autonomous vehicles, viz. self-driving cars, drones,
Autonomous navigation robots (ANR). This work introduces a hybrid learning
model that explores beyond feature fusion and conducts a multimodal weight
sewing strategy towards improving the performance of a baseline SLAM algorithm.
It carries out weight enhancement of the front end feature extractor of the
SLAM via mutation of different deep networks' top layers. At the same time, the
trajectory predictions from independently trained models are amalgamated to
refine the location detail. Thus, the integration of the aforesaid early and
late fusion techniques under a hybrid learning framework minimizes the
translation and rotation errors of the SLAM model. This study exploits some
well-known deep learning (DL) architectures, including ResNet18, ResNet34,
ResNet50, ResNet101, VGG16, VGG19, and AlexNet for experimental analysis. An
extensive experimental analysis proves that hybrid learner (HL) achieves
significantly better results than the unimodal approaches and multimodal
approaches with early or late fusion strategies. Hence, it is found that the
Apolloscape dataset taken in this work has never been used in the literature
under SLAM with fusion techniques, which makes this work unique and insightful.
