# ORBSLAM3-Enhanced Autonomous Toy Drones: Pioneering Indoor Exploration

arXiv: https://arxiv.org/abs/2312.13385 · HTML: https://mlsanity.com/paper/2312.13385/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 49 references that are in the map.
- First posted on arXiv 20 Dec 2023.

## Builds on (most cited first, 19 of 19)

- [ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM](https://mlsanity.com/paper/2007.11898/) (2020)
- [On Coresets for Logistic Regression](https://mlsanity.com/paper/1805.08571/) (2018)
- [Coresets for Near-Convex Functions](https://mlsanity.com/paper/2006.05482/) (2020)
- [Pruning Neural Networks via Coresets and Convex Geometry: Towards No Assumptions](https://mlsanity.com/paper/2209.08554/) (2022)
- [On Coresets for Support Vector Machines](https://mlsanity.com/paper/2002.06469/) (2020)
- [Sets Clustering](https://mlsanity.com/paper/2003.04135/) (2020)
- [New Coresets for Projective Clustering and Applications](https://mlsanity.com/paper/2203.04370/) (2022)
- [Resolving the Approximability of Offline and Online Non-monotone DR-Submodular Maximization over General Convex Sets](https://mlsanity.com/paper/2210.05965/) (2022)
- [From Monocular SLAM to Autonomous Drone Exploration](https://mlsanity.com/paper/1609.07835/) (2016)
- [Using Partial Monotonicity in Submodular Maximization](https://mlsanity.com/paper/2202.03051/) (2022)
- [Asymptotically near-optimal RRT for fast, high-quality, motion planning](https://mlsanity.com/paper/1308.0189/) (2013)
- [Submodular Minimax Optimization: Finding Effective Sets](https://mlsanity.com/paper/2305.16903/) (2023)
- [Coresets for Data Discretization and Sine Wave Fitting](https://mlsanity.com/paper/2203.03009/) (2022)
- [Obstacle Aware Sampling for Path Planning](https://mlsanity.com/paper/2203.04075/) (2022)
- [An Efficient Drifters Deployment Strategy to Evaluate Water Current Velocity Fields](https://mlsanity.com/paper/2301.04216/) (2023)
- [Deep Learning on Home Drone: Searching for the Optimal Architecture](https://mlsanity.com/paper/2209.11064/) (2022)
- [Communication-Aware Multi-robot Coordination with Submodular Maximization](https://mlsanity.com/paper/2011.01476/) (2020)
- [AutoCoreset: An Automatic Practical Coreset Construction Framework](https://mlsanity.com/paper/2305.11980/) (2023)
- [Newton-PnP: Real-time Visual Navigation for Autonomous Toy-Drones](https://mlsanity.com/paper/2203.02686/) (2022)

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

- [SplatMAP: Online Dense Monocular SLAM with 3D Gaussian Splatting](https://mlsanity.com/paper/2501.07015/) (2025)
- [Practical 0.385-Approximation for Submodular Maximization Subject to a Cardinality Constraint](https://mlsanity.com/paper/2405.13994/) (2024)
- [Bridging the Gap Between General and Down-Closed Convex Sets in Submodular Maximization](https://mlsanity.com/paper/2401.09251/) (2024)
- [Supportive Token Revealing for Fast Diffusion Language Model Decoding](https://mlsanity.com/paper/2606.04236/) (2026)
- [Fine-Tuning Diffusion Language Models with Context Selection and Target Weighting](https://mlsanity.com/paper/2609.38385/) (2026)

## Abstract (arXiv)

Navigating toy drones through uncharted GPS-denied indoor spaces poses significant difficulties due to their reliance on GPS for location determination. In such circumstances, the necessity for achieving proper navigation is a primary concern. In response to this formidable challenge, we introduce a real-time autonomous indoor exploration system tailored for drones equipped with a monocular RGB camera. Our system utilizes ORB-SLAM3, a state-of-the-art vision feature-based SLAM, to handle both the localization of toy drones and the mapping of unmapped indoor terrains. Aside from the practicability of ORB-SLAM3, the generated maps are represented as sparse point clouds, making them prone to the presence of outlier data. To address this challenge, we propose an outlier removal algorithm with provable guarantees. Furthermore, our system incorporates a novel exit detection algorithm, ensuring continuous exploration by the toy drone throughout the unfamiliar indoor environment. We also transform the sparse point to ensure proper path planning using existing path planners. To validate the efficacy and efficiency of our proposed system, we conducted offline and real-time experiments on the autonomous exploration of indoor spaces. The results from these endeavors demonstrate the effectiveness of our methods.