CarMap: Fast 3D Feature Map Updates for Automobiles


Fawad Ahmad and Hang Qiu, University of Southern California; Ray Eells, California State Polytechnic University, Pomona; Fan Bai, General Motors; Ramesh Govindan, University of Southern California


Autonomous vehicles need an accurate, up-to-date, 3D map to localize themselves with respect to their surroundings. Today, map collection runs infrequently and uses a fleet of specialized vehicles. In this paper, we explore a different approach: near-real time crowd-sourced 3D map collection from vehicles with advanced sensors (LiDAR, stereo cameras). Our main technical challenge is to find a lean representation of a 3D map such that new map segments, or updates to existing maps, are compact enough to upload in near real-time over a cellular network. To this end, we develop CarMap, which finds a parsimonious representation of a feature map, contains novel object filtering and position-based feature matching techniques to improve localization robustness, and incorporates a novel stitching algorithm to combine map segments from multiple vehicles for unmapped regions and an efficient map-update operation for updating existing map regions. Evaluations show that CarMap takes less than a second (0.6 seconds) to update a map, reduces map sizes by 75× relative to competing strategies, has higher localization accuracy, and is able to localize in corner cases (e.g., multi-lane scenarios) when other approaches fail.

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@inproceedings {246280,
author = {Fawad Ahmad and Hang Qiu and Ray Eells and Fan Bai and Ramesh Govindan},
title = {{CarMap}: Fast 3D Feature Map Updates for Automobiles },
booktitle = {17th USENIX Symposium on Networked Systems Design and Implementation (NSDI 20)},
year = {2020},
isbn = {978-1-939133-13-7},
address = {Santa Clara, CA},
pages = {1063--1081},
url = {},
publisher = {USENIX Association},
month = feb

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