# A Hybrid Learner for Simultaneous Localization and Mapping

arXiv: https://arxiv.org/abs/2101.01158 · HTML: https://mlsanity.com/paper/2101.01158/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 20 references that are in the map.
- First posted on arXiv 4 Jan 2021.

## Builds on (most cited first, 4 of 4)

- [Geometric Loss Functions for Camera Pose Regression with Deep Learning](https://mlsanity.com/paper/1704.00390/) (2017)
- [The ApolloScape Open Dataset for Autonomous Driving and its Application](https://mlsanity.com/paper/1803.06184/) (2018)
- [Multimodal Task-Driven Dictionary Learning for Image Classification](https://mlsanity.com/paper/1502.01094/) (2015)
- [DeLS-3D: Deep Localization and Segmentation with a 3D Semantic Map](https://mlsanity.com/paper/1805.04949/) (2018)

## Abstract (arXiv)

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.