# AdaptSLAM: Edge-Assisted Adaptive SLAM with Resource Constraints via Uncertainty Minimization

arXiv: https://arxiv.org/abs/2301.04620 · HTML: https://mlsanity.com/paper/2301.04620/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jan 2023.

## Built on by (most cited first, 6 of 6)

- [Ultra-High-Frequency Harmony: mmWave Radar and Event Camera Orchestrate Accurate Drone Landing](https://mlsanity.com/paper/2502.14992/) (2025)
- [Map++: Towards User-Participatory Visual SLAM Systems with Efficient Map Expansion and Sharing](https://mlsanity.com/paper/2411.02553/) (2024)
- [User-Centric Communication Service Provision for Edge-Assisted Mobile Augmented Reality](https://mlsanity.com/paper/2509.25905/) (2025)
- [Digital Twin-Based 3D Map Management for Edge-Assisted Mobile Augmented Reality](https://mlsanity.com/paper/2305.16571/) (2023)
- [Improving Efficiency of DNN-based Relocalization Module for Autonomous Driving with Server-side Computing](https://mlsanity.com/paper/2312.00316/) (2023)
- [QoE-Aware Service Provision for Mobile AR Rendering: An Agent-Driven Approach](https://mlsanity.com/paper/2508.08627/) (2025)

## Abstract (arXiv)

Edge computing is increasingly proposed as a solution for reducing resource consumption of mobile devices running simultaneous localization and mapping (SLAM) algorithms, with most edge-assisted SLAM systems assuming the communication resources between the mobile device and the edge server to be unlimited, or relying on heuristics to choose the information to be transmitted to the edge. This paper presents AdaptSLAM, an edge-assisted visual (V) and visual-inertial (VI) SLAM system that adapts to the available communication and computation resources, based on a theoretically grounded method we developed to select the subset of keyframes (the representative frames) for constructing the best local and global maps in the mobile device and the edge server under resource constraints. We implemented AdaptSLAM to work with the state-of-the-art open-source V- and VI-SLAM ORB-SLAM3 framework, and demonstrated that, under constrained network bandwidth, AdaptSLAM reduces the tracking error by 62% compared to the best baseline method.