Modelify: An approach to incrementally build 3D object models for map completion. (March 2023)

• Record Type: Journal Article
• Authors: Furrer, Fadri; Novkovic, Tonci; Fehr, Marius; Grinvald, Margarita; Cadena, Cesar; Nieto, Juan; Siegwart, Roland
• Is Part Of: International journal of robotics research. Volume 42:Number 3(2023)
• Page Start: 45
• Page End: 65
• Publication Date: 2023-03
• Subjects: Incremental object database -- mapping -- object matching -- object merging -- depth data -- truncated signed distance field; Robots -- Periodicals; Robots, Industrial -- Periodicals; 629.89205
• DOI: 10.1177/02783649231166977
• Languages: English
• ISSNs: 0278-3649

Abstract

The capabilities of discovering new knowledge and updating the previously acquired one are crucial for deploying autonomous robots in unknown and changing environments. Spatial and objectness concepts are at the basis of several robotic functionalities and are part of the intuitive understanding of the physical world for us humans. In this paper, we propose a method, which we call Modelify, to incrementally map the environment at the level of objects in a consistent manner. We follow an approach where no prior knowledge of the environment is required. The only assumption we make is that objects in the environment are separated by concave boundaries. The approach works on an RGB-D camera stream, where object-like segments are extracted and stored in an incremental database. Segment description and matching are performed by exploiting 2D and 3D information, allowing to build a graph of all segments. Finally, a matching score guides a Markov clustering algorithm to merge segments, thus completing object representations. Our approach allows creating single (merged) instances of repeating objects, objects that were observed from different viewpoints, and objects that were observed in previous mapping sessions. Thanks to our matching and merging strategies this also works with only partially overlapping segments. We perform evaluations on indoor and outdoor datasets recorded with different RGB-D sensors and show the benefit of using a clustering method to form merge candidates and keypoints detected in both 2D and 3D. Our new method shows better results than previous approaches while being significantly faster. A newly recorded dataset and the source code are released with this publication.