# Mapping While Following: 2D LiDAR SLAM in Indoor Dynamic Environments with a Person Tracker

arXiv: https://arxiv.org/abs/2204.08163 · HTML: https://mlsanity.com/paper/2204.08163/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Apr 2022.

## Built on by (most cited first, 1 of 1)

- [Towards Considerate Embodied AI: Co-Designing Situated Multi-Site Healthcare Robots from Abstract Concepts to High-Fidelity Prototypes](https://mlsanity.com/paper/2602.03054/) (2026)

## Abstract (arXiv)

2D LiDAR SLAM (Simultaneous Localization and Mapping) is widely used in indoor environments due to its stability and flexibility. However, its mapping procedure is usually operated by a joystick in static environments, while indoor environments often are dynamic with moving objects such as people. The generated map with noisy points due to the dynamic objects is usually incomplete and distorted. To address this problem, we propose a framework of 2D-LiDAR-based SLAM without manual control that effectively excludes dynamic objects (people) and simplify the process for a robot to map an environment. The framework, which includes three parts: people tracking, filtering and following. We verify our proposed framework in experiments with two classic 2D-LiDAR-based SLAM algorithms in indoor environments. The results show that this framework is effective in handling dynamic objects and reducing the mapping error.