A benchmark for RGB-D visual odometry, 3D reconstruction and SLAM

TLDR

Existing RGB‑D datasets lack a benchmark for surface reconstruction accuracy, a gap this work addresses. The study introduces the ICL‑NUIM dataset to evaluate RGB‑D visual odometry, 3D reconstruction, and SLAM, with a focus on benchmarking surface reconstruction accuracy. The dataset comprises handheld RGB‑D camera sequences in synthetically generated environments, with perfect ground truth poses and surface models, and includes simulated sensor noise to mimic real‑world artefacts.

Abstract

We introduce the Imperial College London and National University of Ireland Maynooth (ICL-NUIM) dataset for the evaluation of visual odometry, 3D reconstruction and SLAM algorithms that typically use RGB-D data. We present a collection of handheld RGB-D camera sequences within synthetically generated environments. RGB-D sequences with perfect ground truth poses are provided as well as a ground truth surface model that enables a method of quantitatively evaluating the final map or surface reconstruction accuracy. Care has been taken to simulate typically observed real-world artefacts in the synthetic imagery by modelling sensor noise in both RGB and depth data. While this dataset is useful for the evaluation of visual odometry and SLAM trajectory estimation, our main focus is on providing a method to benchmark the surface reconstruction accuracy which to date has been missing in the RGB-D community despite the plethora of ground truth RGB-D datasets available.