A Hybrid Learner for Simultaneous Localization and Mapping

by   Thangarajah Akilan, et al.

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.


page 1

page 4


RGB-Depth SLAM Review

Simultaneous Localization and Mapping (SLAM) have made the real-time den...

Keeping Less is More: Point Sparsification for Visual SLAM

When adapting Simultaneous Mapping and Localization (SLAM) to real-world...

Datasets and Evaluation for Simultaneous Localization and Mapping Related Problems: A Comprehensive Survey

Simultaneous Localization and Mapping (SLAM) has found an increasing uti...

Monocular LSD-SLAM Integration within AR System

In this paper, we cover the process of integrating Large-Scale Direct Si...

TomoSLAM: factor graph optimization for rotation angle refinement in microtomography

In computed tomography (CT), the relative trajectories of a sample, a de...

Random Fourier Features based SLAM

This work is dedicated to simultaneous continuous-time trajectory estima...

Human-Robot Interaction via a Joint-Initiative Supervised Autonomy (JISA) Framework

In this paper, we propose and validate a Joint-Initiative Supervised Aut...