GeoFusion: Geometric Consistency informed Scene Estimation in Dense Clutter
This addresses scene estimation for robots in cluttered environments, but it is incremental as it builds on existing SLAM and perception methods.
The paper tackles the problem of building object-level semantic maps in dense clutter by proposing GeoFusion, a SLAM-based method that uses geometric consistency to improve robustness, resulting in greater robustness against false estimates compared to state-of-the-art frame-by-frame pose estimation.
We propose GeoFusion, a SLAM-based scene estimation method for building an object-level semantic map in dense clutter. In dense clutter, objects are often in close contact and severe occlusions, which brings more false detections and noisy pose estimates from existing perception methods. To solve these problems, our key insight is to consider geometric consistency at the object level within a general SLAM framework. The geometric consistency is defined in two parts: geometric consistency score and geometric relation. The geometric consistency score describes the compatibility between object geometry model and observation point cloud. Meanwhile, it provides a reliable measure to filter out false positives in data association. The geometric relation represents the relationship (e.g. contact) between geometric features (e.g. planes) among objects. The geometric relation makes the graph optimization for poses more robust and accurate. GeoFusion can robustly and efficiently infer the object labels, 6D object poses, and spatial relations from continuous noisy semantic measurements. We quantitatively evaluate our method using observations from a Fetch mobile manipulation robot. Our results demonstrate greater robustness against false estimates than frame-by-frame pose estimation from the state-of-the-art convolutional neural network.