# Unifying Map and Landmark Based Representations for Visual Navigation

arXiv: https://arxiv.org/abs/1712.08125 · HTML: https://mlsanity.com/paper/1712.08125/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Dec 2017.

## Built on by (most cited first, 10 of 10)

- [A Survey of Embodied AI: From Simulators to Research Tasks](https://mlsanity.com/paper/2103.04918/) (2021)
- [SASRA: Semantically-aware Spatio-temporal Reasoning Agent for Vision-and-Language Navigation in Continuous Environments](https://mlsanity.com/paper/2108.11945/) (2021)
- [Learning in Audio-visual Context: A Review, Analysis, and New Perspective](https://mlsanity.com/paper/2208.09579/) (2022)
- [Following High-level Navigation Instructions on a Simulated Quadcopter with Imitation Learning](https://mlsanity.com/paper/1806.00047/) (2018)
- [Last-Mile Embodied Visual Navigation](https://mlsanity.com/paper/2211.11746/) (2022)
- [Causal Navigation by Continuous-time Neural Networks](https://mlsanity.com/paper/2106.08314/) (2021)
- [MaAST: Map Attention with Semantic Transformersfor Efficient Visual Navigation](https://mlsanity.com/paper/2103.11374/) (2021)
- [GraphMapper: Efficient Visual Navigation by Scene Graph Generation](https://mlsanity.com/paper/2205.08325/) (2022)
- [Find a Way Forward: a Language-Guided Semantic Map Navigator](https://mlsanity.com/paper/2203.03183/) (2022)
- [Pseudo-Trilateral Adversarial Training for Domain Adaptive Traversability Prediction](https://mlsanity.com/paper/2306.14370/) (2023)

## Abstract (arXiv)

This works presents a formulation for visual navigation that unifies map based spatial reasoning and path planning, with landmark based robust plan execution in noisy environments. Our proposed formulation is learned from data and is thus able to leverage statistical regularities of the world. This allows it to efficiently navigate in novel environments given only a sparse set of registered images as input for building representations for space. Our formulation is based on three key ideas: a learned path planner that outputs path plans to reach the goal, a feature synthesis engine that predicts features for locations along the planned path, and a learned goal-driven closed loop controller that can follow plans given these synthesized features. We test our approach for goal-driven navigation in simulated real world environments and report performance gains over competitive baseline approaches.