# Learn to Navigate Maplessly with Varied LiDAR Configurations: A Support Point-Based Approach

arXiv: https://arxiv.org/abs/2010.10209 · HTML: https://mlsanity.com/paper/2010.10209/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Oct 2020.

## Built on by (most cited first, 5 of 5)

- [IPAPRec: A promising tool for learning high-performance mapless navigation skills with deep reinforcement learning](https://mlsanity.com/paper/2103.11686/) (2021)
- [Combining RL and IL using a dynamic, performance-based modulation over learning signals and its application to local planning](https://mlsanity.com/paper/2405.09760/) (2024)
- [Analysis of LiDAR Configurations on Off-road Semantic Segmentation Performance](https://mlsanity.com/paper/2306.16551/) (2023)
- [Enhancing Deep Reinforcement Learning-based Robot Navigation Generalization through Scenario Augmentation](https://mlsanity.com/paper/2503.01146/) (2025)
- [Learning from Demonstration with Failure Awareness for Safe Robot Navigation](https://mlsanity.com/paper/2604.23360/) (2026)

## Abstract (arXiv)

Deep reinforcement learning (DRL) demonstrates great potential in mapless navigation domain. However, such a navigation model is normally restricted to a fixed configuration of the range sensor because its input format is fixed. In this paper, we propose a DRL model that can address range data obtained from different range sensors with different installation positions. Our model first extracts the goal-directed features from each obstacle point. Subsequently, it chooses global obstacle features from all point-feature candidates and uses these features for the final decision. As only a few points are used to support the final decision, we refer to these points as support points and our approach as support point-based navigation (SPN). Our model can handle data from different LiDAR setups and demonstrates good performance in simulation and real-world experiments. Moreover, it shows great potential in crowded scenarios with small obstacles when using a high-resolution LiDAR.