# Object Goal Navigation Based on Semantics and RGB Ego View

arXiv: https://arxiv.org/abs/2210.11543 · HTML: https://mlsanity.com/paper/2210.11543/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 13 references that are in the map.
- First posted on arXiv 20 Oct 2022.

## Builds on (most cited first, 6 of 6)

- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations](https://mlsanity.com/paper/1602.07332/) (2016)
- [The Revisiting Problem in Simultaneous Localization and Mapping: A Survey on Visual Loop Closure Detection](https://mlsanity.com/paper/2204.12831/) (2022)
- [Object Goal Navigation using Data Regularized Q-Learning](https://mlsanity.com/paper/2208.13009/) (2022)
- [Spatial Relation Graph and Graph Convolutional Network for Object Goal Navigation](https://mlsanity.com/paper/2208.13031/) (2022)

## Built on by (most cited first, 2 of 2)

- [One Map to Find Them All: Real-time Open-Vocabulary Mapping for Zero-shot Multi-Object Navigation](https://mlsanity.com/paper/2409.11764/) (2024)
- [Sequence-Agnostic Multi-Object Navigation](https://mlsanity.com/paper/2305.06178/) (2023)

## Abstract (arXiv)

This paper presents an architecture and methodology to empower a service robot to navigate an indoor environment with semantic decision making, given RGB ego view. This method leverages the knowledge of robot's actuation capability and that of scenes, objects and their relations -- represented in a semantic form. The robot navigates based on GeoSem map - a relational combination of geometric and semantic map. The goal given to the robot is to find an object in a unknown environment with no navigational map and only egocentric RGB camera perception. The approach is tested both on a simulation environment and real life indoor settings. The presented approach was found to outperform human users in gamified evaluations with respect to average completion time.