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Humans are able to form a complex mental model of the environment they movein. This mental model captures geometric and semantic aspects of the scene,describes the environment at multiple levels of abstractions (e.g., objects,rooms, buildings), includes static and dynamic entities and their relations(e.g., a person is in a room at a given time). In contrast, current robots'internal representations still provide a partial and fragmented understandingof the environment, either in the form of a sparse or dense set of geometricprimitives (e.g., points, lines, planes, voxels) or as a collection of objects.This paper attempts to reduce the gap between robot and human perception byintroducing a novel representation, a 3D Dynamic Scene Graph(DSG), thatseamlessly captures metric and semantic aspects of a dynamic environment. A DSGis a layered graph where nodes represent spatial concepts at different levelsof abstraction, and edges represent spatio-temporal relations among nodes. Oursecond contribution is Kimera, the first fully automatic method to build a DSGfrom visual-inertial data. Kimera includes state-of-the-art techniques forvisual-inertial SLAM, metric-semantic 3D reconstruction, object localization,human pose and shape estimation, and scene parsing. Our third contribution is acomprehensive evaluation of Kimera in real-life datasets and photo-realisticsimulations, including a newly released dataset, uHumans2, which simulates acollection of crowded indoor and outdoor scenes. Our evaluation shows thatKimera achieves state-of-the-art performance in visual-inertial SLAM, estimatesan accurate 3D metric-semantic mesh model in real-time, and builds a DSG of acomplex indoor environment with tens of objects and humans in minutes. Ourfinal contribution shows how to use a DSG for real-time hierarchical semanticpath-planning. The core modules in Kimera are open-source.

Kimera: from SLAM to Spatial Perception with 3D Dynamic Scene Graphs