Occupancy Anticipation for Efficient Exploration and Navigation

By Santhosh K. Ramakrishnan, Ziad Al-Halah, Kristen Grauman. 2020

204 citations recorded by the source corpus.

State-of-the-art navigation methods leverage a spatial memory to generalize to new environments, but their occupancy maps are limited to capturing the geometric structures directly observed by the agent. We propose occupancy anticipation, where the agent uses its egocentric RGB-D observations to infer the occupancy state beyond the visible regions. In doing so, the agent builds its spatial awareness more rapidly, which facilitates efficient exploration and navigation in 3D environments. By exploiting context in both the egocentric views and top-down maps our model successfully anticipates a broader map of the environment, with performance significantly better than strong baselines. Furthermore, when deployed for the sequential decision-making tasks of exploration and navigation, our model outperforms state-of-the-art methods on the Gibson and Matterport3D datasets. Our approach is the winning entry in the 2020 Habitat PointNav Challenge. Project page: this http URL

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Reinforcement Learning, Robotics & Control

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Citations in the corpus

Differentiable SLAM-net: Learning Particle SLAM for Visual Navigation (2021, methodology): “Neural SLAM [10; 51] addresses the joint SLAM problem, but it relies solely on relative visual odometry without local bundle adjustment or loop closure, and thus it inherently accumulates errors over time.”
The ThreeDWorld Transport Challenge: A Visually Guided Task-and-Motion Planning Benchmark Towards Physically Realistic Embodied AI (2021, unknown): “…AI2-THOR[25], HoME [44], Virtual-Home [27], Habitat [31], Gibson [47], Deepmind Lab [4], RoboTHOR [14], SAPIEN [48], and TDW [15] have facilitated novel algorithms in visual navigation [52, 31, 28], visual-language navigation [1, 41, 33], embodied question answering [13, 19] and other tasks.”
A Survey on Active Simultaneous Localization and Mapping: State of the Art and New Frontiers (2022, background): “[232] directly predict augmented OG maps beyond the sensor’s field-of-view using autoencoders (AE).”
Visual Room Rearrangement (2021, background): “Various tasks have been proposed in this domain: navigation towards objects [4, 51, 48, 7] or towards a speciﬁc point [1, 38, 47, 36], scene exploration [9, 8], embodied question answering [18, 13], task completion [55], instruction following [2, 41], object manipulation [16, 52], multi-agent…”