"The Pedestrian next to the Lamppost" Adaptive Object Graphs for Better Instantaneous Mapping

• URL: http://arxiv.org/abs/2204.02944v1
• Date: Wed, 6 Apr 2022 17:23:13 GMT
• Title: "The Pedestrian next to the Lamppost" Adaptive Object Graphs for Better Instantaneous Mapping
• Authors: Avishkar Saha, Oscar Mendez, Chris Russell, Richard Bowden
• Abstract summary: Estimating a semantically segmented bird's-eye-view map from a single image has become a popular technique for autonomous control and navigation. We show an increase in localization error with distance from the camera. We propose a graph neural network which predicts BEV objects from a monocular image by spatially reasoning about an object within the context of other objects.
• Score: 45.94778766867247
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Estimating a semantically segmented bird's-eye-view (BEV) map from a single image has become a popular technique for autonomous control and navigation. However, they show an increase in localization error with distance from the camera. While such an increase in error is entirely expected - localization is harder at distance - much of the drop in performance can be attributed to the cues used by current texture-based models, in particular, they make heavy use of object-ground intersections (such as shadows), which become increasingly sparse and uncertain for distant objects. In this work, we address these shortcomings in BEV-mapping by learning the spatial relationship between objects in a scene. We propose a graph neural network which predicts BEV objects from a monocular image by spatially reasoning about an object within the context of other objects. Our approach sets a new state-of-the-art in BEV estimation from monocular images across three large-scale datasets, including a 50% relative improvement for objects on nuScenes.

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