# Introducing SLAMBench, a performance and accuracy benchmarking methodology for SLAM

arXiv: https://arxiv.org/abs/1410.2167 · HTML: https://mlsanity.com/paper/1410.2167/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Oct 2014.

## Built on by (most cited first, 9 of 9)

- [Deep Multi-Task Learning for Joint Localization, Perception, and Prediction](https://mlsanity.com/paper/2101.06720/) (2021)
- [Nerfels: Renderable Neural Codes for Improved Camera Pose Estimation](https://mlsanity.com/paper/2206.01916/) (2022)
- [UDA-Bench: Revisiting Common Assumptions in Unsupervised Domain Adaptation Using a Standardized Framework](https://mlsanity.com/paper/2409.15264/) (2024)
- [VSLAM-LAB: A Comprehensive Framework for Visual SLAM Methods and Datasets](https://mlsanity.com/paper/2504.04457/) (2025)
- [CODEI: Resource-Efficient Task-Driven Co-Design of Perception and Decision Making for Mobile Robots Applied to Autonomous Vehicles](https://mlsanity.com/paper/2503.10296/) (2025)
- [SLAM Adversarial Lab: An Extensible Framework for Visual SLAM Robustness Evaluation under Adverse Conditions](https://mlsanity.com/paper/2603.17165/) (2026)
- [Systematic Comparison of Path Planning Algorithms using PathBench](https://mlsanity.com/paper/2203.03092/) (2022)
- [Lost in Tracking Translation: A Comprehensive Analysis of Visual SLAM in Human-Centered XR and IoT Ecosystems](https://mlsanity.com/paper/2411.07146/) (2024)
- [Accelerating Transformer-Based Monocular SLAM via Geometric Utility Scoring](https://mlsanity.com/paper/2604.08718/) (2026)

## Abstract (arXiv)

Real-time dense computer vision and SLAM offer great potential for a new level of scene modelling, tracking and real environmental interaction for many types of robot, but their high computational requirements mean that use on mass market embedded platforms is challenging. Meanwhile, trends in low-cost, low-power processing are towards massive parallelism and heterogeneity, making it difficult for robotics and vision researchers to implement their algorithms in a performance-portable way. In this paper we introduce SLAMBench, a publicly-available software framework which represents a starting point for quantitative, comparable and validatable experimental research to investigate trade-offs in performance, accuracy and energy consumption of a dense RGB-D SLAM system. SLAMBench provides a KinectFusion implementation in C++, OpenMP, OpenCL and CUDA, and harnesses the ICL-NUIM dataset of synthetic RGB-D sequences with trajectory and scene ground truth for reliable accuracy comparison of different implementation and algorithms. We present an analysis and breakdown of the constituent algorithmic elements of KinectFusion, and experimentally investigate their execution time on a variety of multicore and GPUaccelerated platforms. For a popular embedded platform, we also present an analysis of energy efficiency for different configuration alternatives.