# iRotate: Active Visual SLAM for Omnidirectional Robots

arXiv: https://arxiv.org/abs/2103.11641 · HTML: https://mlsanity.com/paper/2103.11641/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Mar 2021.

## Built on by (most cited first, 3 of 3)

- [Visual SLAM: What are the Current Trends and What to Expect?](https://mlsanity.com/paper/2210.10491/) (2022)
- [Grid Cell-Inspired Fragmentation and Recall for Efficient Map Building](https://mlsanity.com/paper/2307.05793/) (2023)
- [FIT-SLAM -- Fisher Information and Traversability estimation-based Active SLAM for exploration in 3D environments](https://mlsanity.com/paper/2401.09322/) (2024)

## Abstract (arXiv)

In this paper, we present an active visual SLAM approach for omnidirectional robots. The goal is to generate control commands that allow such a robot to simultaneously localize itself and map an unknown environment while maximizing the amount of information gained and consuming as low energy as possible. Leveraging the robot's independent translation and rotation control, we introduce a multi-layered approach for active V-SLAM. The top layer decides on informative goal locations and generates highly informative paths to them. The second and third layers actively re-plan and execute the path, exploiting the continuously updated map and local features information. Moreover, we introduce two utility formulations to account for the presence of obstacles in the field of view and the robot's location. Through rigorous simulations, real robot experiments, and comparisons with state-of-the-art methods, we demonstrate that our approach achieves similar coverage results with lesser overall map entropy. This is obtained while keeping the traversed distance up to 39% shorter than the other methods and without increasing the wheels' total rotation amount. Code and implementation details are provided as open-source, and all the generated data is available on-line for consultation.