MR6D: Benchmarking 6D Pose Estimation for Mobile Robots
This addresses the need for better pose estimation benchmarks for mobile robotics in industrial applications, though it is incremental as it primarily provides a new dataset rather than a novel method.
The authors tackled the problem that existing 6D pose estimation datasets focus on small household objects for robot arms, which limits relevance to mobile robots that face different challenges like long-range perception and heavy occlusion. They introduced MR6D, a dataset for mobile robots in industrial environments with 92 scenes and 16 objects, and found that current pipelines underperform in these settings.
Existing 6D pose estimation datasets primarily focus on small household objects typically handled by robot arm manipulators, limiting their relevance to mobile robotics. Mobile platforms often operate without manipulators, interact with larger objects, and face challenges such as long-range perception, heavy self-occlusion, and diverse camera perspectives. While recent models generalize well to unseen objects, evaluations remain confined to household-like settings that overlook these factors. We introduce MR6D, a dataset designed for 6D pose estimation for mobile robots in industrial environments. It includes 92 real-world scenes featuring 16 unique objects across static and dynamic interactions. MR6D captures the challenges specific to mobile platforms, including distant viewpoints, varied object configurations, larger object sizes, and complex occlusion/self-occlusion patterns. Initial experiments reveal that current 6D pipelines underperform in these settings, with 2D segmentation being another hurdle. MR6D establishes a foundation for developing and evaluating pose estimation methods tailored to the demands of mobile robotics. The dataset is available at https://huggingface.co/datasets/anas-gouda/mr6d.