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arXiv · 2204.02944

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

Avishkar Saha·Oscar Mendez·Chris Russell·Richard Bowden
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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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Avishkar Saha, Oscar Mendez, Chris Russell, Richard Bowden. 2022-04-06. "The Pedestrian next to the Lamppost" Adaptive Object Graphs for Better Instantaneous Mapping. https://arxiv.org/abs/2204.02944

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