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Ultimate SLAM? Combining Events, Images, and IMU for Robust Visual SLAM in HDR and High Speed Scenarios


Rosinol Vidal, Antonio; Rebecq, Henri; Horstschaefer, Timo; Scaramuzza, Davide (2018). Ultimate SLAM? Combining Events, Images, and IMU for Robust Visual SLAM in HDR and High Speed Scenarios. IEEE Robotics and Automation Letters, 3(2):994-1001.

Abstract

Event cameras are bioinspired vision sensors that output pixel-level brightness changes instead of standard intensity frames. These cameras do not suffer from motion blur and have a very high dynamic range, which enables them to provide reliable visual information during high-speed motions or in scenes characterized by high dynamic range. However, event cameras output only little information when the amount of motion is limited, such as in the case of almost still motion. Conversely, standard cameras provide instant and rich information about the environment most of the time (in low-speed and good lighting scenarios), but they fail severely in case of fast motions, or difficult lighting such as high dynamic range or low light scenes. In this letter, we present the first state estimation pipeline that leverages the complementary advantages of these two sensors by fusing in a tightly coupled manner events, standard frames, and inertial measurements. We show on the publicly available Event Camera Dataset that our hybrid pipeline leads to an accuracy improvement of 130% over event-only pipelines, and 85% over standard-frames-only visual-inertial systems, while still being computationally tractable. Furthermore, we use our pipeline to demonstrate-to the best of our knowledge-the first autonomous quadrotor flight using an event camera for state estimation, unlocking flight scenarios that were not reachable with traditional visual-inertial odometry, such as low-light environments and high dynamic range scenes. Videos of the experiments: ttp://rpg.ifi.uzh.ch/ultimateslam.html

Abstract

Event cameras are bioinspired vision sensors that output pixel-level brightness changes instead of standard intensity frames. These cameras do not suffer from motion blur and have a very high dynamic range, which enables them to provide reliable visual information during high-speed motions or in scenes characterized by high dynamic range. However, event cameras output only little information when the amount of motion is limited, such as in the case of almost still motion. Conversely, standard cameras provide instant and rich information about the environment most of the time (in low-speed and good lighting scenarios), but they fail severely in case of fast motions, or difficult lighting such as high dynamic range or low light scenes. In this letter, we present the first state estimation pipeline that leverages the complementary advantages of these two sensors by fusing in a tightly coupled manner events, standard frames, and inertial measurements. We show on the publicly available Event Camera Dataset that our hybrid pipeline leads to an accuracy improvement of 130% over event-only pipelines, and 85% over standard-frames-only visual-inertial systems, while still being computationally tractable. Furthermore, we use our pipeline to demonstrate-to the best of our knowledge-the first autonomous quadrotor flight using an event camera for state estimation, unlocking flight scenarios that were not reachable with traditional visual-inertial odometry, such as low-light environments and high dynamic range scenes. Videos of the experiments: ttp://rpg.ifi.uzh.ch/ultimateslam.html

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Language:English
Date:1 April 2018
Deposited On:22 Mar 2018 12:33
Last Modified:13 Apr 2018 11:43
Publisher:Institute of Electrical and Electronics Engineers
ISSN:2377-3766
OA Status:Green
Free access at:Official URL. An embargo period may apply.
Publisher DOI:https://doi.org/10.1109/lra.2018.2793357
Official URL:http://rpg.ifi.uzh.ch/docs/RAL18_VidalRebecq.pdf
Other Identification Number:merlin-id:16266

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