# Benchmarking Classic and Learned Navigation in Complex 3D Environments

arXiv: https://arxiv.org/abs/1901.10915 · HTML: https://mlsanity.com/paper/1901.10915/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jan 2019.

## Built on by (most cited first, 24 of 24)

- [A Survey of Embodied AI: From Simulators to Research Tasks](https://mlsanity.com/paper/2103.04918/) (2021)
- [Navigating to Objects in the Real World](https://mlsanity.com/paper/2212.00922/) (2022)
- [SayNav: Grounding Large Language Models for Dynamic Planning to Navigation in New Environments](https://mlsanity.com/paper/2309.04077/) (2023)
- [Learning to Map for Active Semantic Goal Navigation](https://mlsanity.com/paper/2106.15648/) (2021)
- [Move2Hear: Active Audio-Visual Source Separation](https://mlsanity.com/paper/2105.07142/) (2021)
- [Differentiable SLAM-net: Learning Particle SLAM for Visual Navigation](https://mlsanity.com/paper/2105.07593/) (2021)
- [Uncertainty-driven Planner for Exploration and Navigation](https://mlsanity.com/paper/2202.11907/) (2022)
- [Zero-shot object goal visual navigation](https://mlsanity.com/paper/2206.07423/) (2022)
- [Differentiable Spatial Planning using Transformers](https://mlsanity.com/paper/2112.01010/) (2021)
- [A Survey on Human-aware Robot Navigation](https://mlsanity.com/paper/2106.11650/) (2021)
- [SelfD: Self-Learning Large-Scale Driving Policies From the Web](https://mlsanity.com/paper/2204.10320/) (2022)
- [Teaching Agents how to Map: Spatial Reasoning for Multi-Object Navigation](https://mlsanity.com/paper/2107.06011/) (2021)
- [MaAST: Map Attention with Semantic Transformersfor Efficient Visual Navigation](https://mlsanity.com/paper/2103.11374/) (2021)
- [Embodied AI-Driven Operation of Smart Cities: A Concise Review](https://mlsanity.com/paper/2108.09823/) (2021)
- [GraphMapper: Efficient Visual Navigation by Scene Graph Generation](https://mlsanity.com/paper/2205.08325/) (2022)
- [Modality-invariant Visual Odometry for Embodied Vision](https://mlsanity.com/paper/2305.00348/) (2023)
- [SGoLAM: Simultaneous Goal Localization and Mapping for Multi-Object Goal Navigation](https://mlsanity.com/paper/2110.07171/) (2021)
- [Good Actions Succeed, Bad Actions Generalize: A Case Study on Why RL Generalizes Better](https://mlsanity.com/paper/2503.15693/) (2025)
- [Symmetry-aware Neural Architecture for Embodied Visual Navigation](https://mlsanity.com/paper/2112.09515/) (2021)
- [Task-Driven Graph Attention for Hierarchical Relational Object Navigation](https://mlsanity.com/paper/2306.13760/) (2023)
- [Transformers for Image-Goal Navigation](https://mlsanity.com/paper/2405.14128/) (2024)
- [Structured Exploration Through Instruction Enhancement for Object Navigation](https://mlsanity.com/paper/2211.08467/) (2022)
- [DEUX: Active Exploration for Learning Unsupervised Depth Perception](https://mlsanity.com/paper/2310.06164/) (2023)
- [Insect-inspired Visual Point-goal Navigation](https://mlsanity.com/paper/2601.16806/) (2026)

## Abstract (arXiv)

Navigation research is attracting renewed interest with the advent of learning-based methods. However, this new line of work is largely disconnected from well-established classic navigation approaches. In this paper, we take a step towards coordinating these two directions of research. We set up classic and learning-based navigation systems in common simulated environments and thoroughly evaluate them in indoor spaces of varying complexity, with access to different sensory modalities. Additionally, we measure human performance in the same environments. We find that a classic pipeline, when properly tuned, can perform very well in complex cluttered environments. On the other hand, learned systems can operate more robustly with a limited sensor suite. Overall, both approaches are still far from human-level performance.