The Oxford Multimotion Dataset: Multiple SE(3) Motions with Ground Truth (1901.01445v2)

Abstract: Datasets advance research by posing challenging new problems and providing standardized methods of algorithm comparison. High-quality datasets exist for many important problems in robotics and computer vision, including egomotion estimation and motion/scene segmentation, but not for techniques that estimate every motion in a scene. Metric evaluation of these multimotion estimation techniques requires datasets consisting of multiple, complex motions that also contain ground truth for every moving body. The Oxford Multimotion Dataset provides a number of multimotion estimation problems of varying complexity. It includes both complex problems that challenge existing algorithms as well as a number of simpler problems to support development. These include observations from both static and dynamic sensors, a varying number of moving bodies, and a variety of different 3D motions. It also provides a number of experiments designed to isolate specific challenges of the multimotion problem, including rotation about the optical axis and occlusion. In total, the Oxford Multimotion Dataset contains over 110 minutes of multimotion data consisting of stereo and RGB-D camera images, IMU data, and Vicon ground-truth trajectories. The dataset culminates in a complex toy car segment representative of many challenging real-world scenarios. This paper describes each experiment with a focus on its relevance to the multimotion estimation problem.

• The paper’s main contribution is the introduction of a multimotion dataset with precise ground truth for dynamic SE(3) motions in complex scenes.
• It employs a calibrated sensor platform capturing stereo, RGB-D, and IMU data to support robust multimodal motion estimation.
• The dataset facilitates benchmarking and development of advanced SLAM and motion segmentation algorithms for robotics vision.

An Expert Overview of the Oxford Multimotion Dataset

The publication of high-quality datasets often acts as a catalyst for advancements in the fields of robotics and computer vision, providing standardized benchmarks for the development and evaluation of new algorithms. The paper "The Oxford Multimotion Dataset: Multiple SE(3) Motions with Ground Truth" introduces a dataset crafted to address the multimotion estimation problem, a complex aspect of robotic vision requiring the simultaneous estimation of multiple independent motions within a scene.

Overview of the Dataset

The Oxford Multimotion Dataset is distinctive in its provision of ground truth for every moving object in each captured scene, which is critical for the metric evaluation of multimotion estimation techniques. The dataset comprises over 110 minutes of multimodal data, including stereo and RGB-D camera images, IMU data, and precise Vicon ground truth trajectories, thereby representing multiple dynamic environments encountered in real-world applications. This dataset advances multimotion estimation challenges by offering a variety of datasets differing in complexity, from straightforward scenarios to intricate scenes involving occlusion and rotations about the optical axis.

Technical Merits and Contributions

Several salient features distinguish this dataset from existing ones:

• Comprehensive Ground Truth Coverage: In contrast to other datasets such as KITTI or TUM RGB-D, the Oxford Multimotion Dataset uniquely provides extensive ground truth data for multiple moving objects in scenes, enhancing the datasets' utility for full multimotion estimation rather than only addressing egomotion or scene segmentation.
• Diverse Sensor Data: The collected datasets include calibrated stereo images, RGB-D data, and IMU readings, fostering the development of multimodal approaches in motion estimation.
• Calibrated Experimental Setup: Using a meticulously calibrated sensor platform, the dataset ensures high fidelity in the captured data, thus reducing error margins in estimation tasks and enhancing the reproducibility of results.

Implications and Prospective Application

The implications of this dataset are manifold, both in practical robotics applications and theoretical advancements in computer vision and motion estimation:

• Algorithm Benchmarking: Researchers can utilize the Oxford Multimotion Dataset to benchmark and fine-tune algorithms for simultaneous localization and mapping (SLAM), tracking, and motion segmentation in dynamic and complex environments.
• Robotics Vision Enhancement: Real-world robotics applications, such as autonomous navigation and robotic perception systems, can benefit from models trained and tested using this dataset by improving performance in dynamic and multimotion-filled scenarios.
• Theoretical Exploration: The dataset provides rich opportunities for the exploration and development of robust multimotion estimation algorithms, potentially leading to methods that significantly enhance the robustness and accuracy of visual odometry, especially in scenes with overlapping motions and varying motion dynamics.

Future Directions for Multimotion Estimation

The Oxford Multimotion Dataset lays the groundwork for addressing challenges in multimotion estimation and suggests several avenues for future research and development. Enhanced algorithms capable of handling increased complexity, such as dense occlusions or higher numbers of independent motions, stand as a natural progression. Moreover, the dataset could inspire the integration of advanced machine learning techniques, including deep learning, for developing data-driven approaches to motion segmentation and estimation.

Conclusively, the Oxford Multimotion Dataset represents a strategic tool for researchers aiming to push the boundaries in robotic vision, offering a structured challenge to bolster the design of sophisticated algorithms capable of managing multimotion environments effectively.