rgb d斯拉姆数据集和基准(rgbd slam dataset and benchmark)

RGB - D斯拉姆数据集和基准(RGB-D SLAM Dataset

and Benchmark)

数据介绍：

We provide a large dataset containing RGB-D data and ground-truth data with the goal to establish a novel benchmark for the evaluation of visual odometry and visual SLAM systems. Our dataset contains the color and depth images of a Microsoft Kinect sensor along the

ground-truth trajectory of the sensor. The data was recorded at full frame rate (30 Hz) and sensor resolution (640×480). The ground-truth trajectory was obtained from a high-accuracy motion-capture system with eight high-speed tracking cameras (100 Hz). Further, we provide the accelerometer data from the Kinect. Finally, we propose an evaluation criterion for measuring the quality of the estimated camera trajectory of visual SLAM systems.

关键词：

RGB-D,地面实况,基准,测程,轨迹,

RGB-D,ground-truth,benchmark,odometry,trajectory,

数据格式：

IMAGE

数据详细介绍：

RGB-D SLAM Dataset and Benchmark

Contact: Jürgen Sturm

We provide a large dataset containing RGB-D data and ground-truth data with the goal to establish a novel benchmark for the evaluation of visual odometry and visual SLAM systems. Our dataset contains the color and depth images of a Microsoft Kinect sensor along the ground-truth trajectory of the sensor. The data was recorded at full frame rate (30 Hz) and sensor resolution (640×480). The ground-truth trajectory was obtained from a high-accuracy motion-capture system with eight high-speed tracking cameras (100 Hz). Further, we provide the accelerometer data from the Kinect. Finally, we propose an evaluation criterion for measuring the quality of the estimated camera trajectory of visual SLAM systems.

How can I use the RGB-D Benchmark to evaluate my SLAM system?

1. Download one or more of the RGB-D benchmark sequences (file

formats, useful tools)

2. Run your favorite visual odometry/visual SLAM algorithm (for example,

RGB-D SLAM)

3. Save the estimated camera trajectory to a file (file formats, example

trajectory)

4. Evaluate your algorithm by comparing the estimated trajectory with the

ground truth trajectory. We provide an automated evaluation tool to help you with the evaluation. There is also an online version of the tool. Further remarks

Jose Luis Blanco has added our dataset to the mobile robot programming toolkit (MRPT) repository. The dataset (including example code and tools) can be downloaded here.

∙If you have any questions about the dataset/benchmark/evaluation/file formats, please don't hesitate to contact Jürgen Sturm.

∙We are happy to share our data with other researchers. Please refer to the respective publication when using this data.

Related publications

2011

Conference and Workshop Papers

Real-Time Visual Odometry from Dense RGB-D Images (F. Steinbruecker, J. Sturm, D. Cremers), In Workshop on Live Dense Reconstruction with Moving Cameras at the Intl. Conf. on Computer Vision (ICCV), 2011. [bib] [pdf] Towards a benchmark for RGB-D SLAM evaluation (J. Sturm, S. Magnenat, N. Engelhard, F. Pomerleau, F. Colas, W. Burgard, D. Cremers, R. Siegwart), In Proc. of the RGB-D Workshop on Advanced Reasoning with Depth Cameras at Robotics: Science and Systems Conf. (RSS), 2011. [bib] [pdf] [pdf]

Real-time 3D visual SLAM with a hand-held camera (N. Engelhard, F. Endres, J. Hess, J. Sturm, W. Burgard), In Proc. of the RGB-D Workshop on 3D Perception in Robotics at the European Robotics Forum, 2011. [bib] [pdf] [video] [video] [video]

File Formats

We provide the RGB-D datasets from the Kinect in the following format:

Color images and depth maps

We provide the time-stamped color and depth images as a gzipped tar file (TGZ).

∙The color images are stored as 640×480 8-bit RGB images in PNG format.

∙The depth maps are stored as 640×480 16-bit monochrome images in PNG format.

∙The color and depth images are already pre-registered using the OpenNI driver from PrimeSense, i.e., the pixels in the color and depth

images correspond already 1:1.

∙The depth images are scaled by a factor of 5000, i.e., a pixel value of 5000 in the depth image corresponds to a distance of 1 meter from the

camera, 10000 to 2 meter distance, etc. A pixel value of 0 means

missing value/no data.