FutureMapping: The Computational Structure of Spatial AI Systems

By Andrew J. Davison. 2018

82 citations recorded by the source corpus.

We discuss and predict the evolution of Simultaneous Localisation and Mapping (SLAM) into a general geometric and semantic `Spatial AI' perception capability for intelligent embodied devices. A big gap remains between the visual perception performance that devices such as augmented reality eyewear or comsumer robots will require and what is possible within the constraints imposed by real products. Co-design of algorithms, processors and sensors will be needed. We explore the computational structure of current and future Spatial AI algorithms and consider this within the landscape of ongoing hardware developments.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Human-Computer Interaction

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Citations in the corpus

RLBench: The Robot Learning Benchmark & Learning Environment (2019, background): “of complex, long-term robot behaviour is a very long one, and that there is great value in combining learning with traditionally engineered methods for scene representation [56].”
Rearrangement: A Challenge for Embodied AI (2020, background): “Investigating agent memory representations is a topic of great interest in the machine learning and Embod-ied AI communities [3, 17, 19].”
Kimera: From SLAM to spatial perception with 3D dynamic scene graphs (2021, background): “, 2015), research in these two fields has traditionally proceeded in isolation, and there has been a recent and growing research at the intersection of these areas (Bao and Savarese, 2011; Bowman et al., 2017; Cadena et al., 2016; Davison, 2018; Grinvald et al., 2019; Hackel et al., 2017; Zheng et a”