NDLI: A Power-Performance Approach to Comparing Sensor Families, with application to comparing neuromorphic to traditional vision sensors

Content Provider	IEEE Xplore Digital Library
Author	Censi, A. Mueller, E. Frazzoli, E. Soatto, S.
Copyright Year	2015
Description	Author affiliation: Lab. for Inf. & Decision Syst., Massachusetts Inst. of Technol., Cambridge, MA, USA (Censi, A.) \|\| Dept. of Aeronaut. & Astronaut., Massachusetts Inst. of Technol., Cambridge, MA, USA (Mueller, E.; Frazzoli, E.) \|\| Dept. of Comput. Sci., Univ. of California Los Angeles, Los Angeles, CA, USA (Soatto, S.)
Abstract	There is considerable freedom in choosing the sensors to be equipped on a robot. Currently many sensing technologies are available (radar, lidar, vision sensors, time-of-flight cameras, etc.). For each class, there are additional choices regarding the exact sensor parameters (spatial resolution, frame rate, etc.). Which sensor is best? In general, this question needs to be qualified. It depends on the task. In an estimation task, the answer depends on the prior for the signal. In a control task, the answer depends exactly on which are the sufficient statistics for computing the control signal. This paper shows that an ulterior qualification that needs to be made: the answer depends on the power available for sensing, even when the task is fixed. We define the “power-performance” curve as the performance attainable on a task for a given level of sensing power. We show that this approach is well suited to comparing a traditional CMOS sensor with the recently available “neuromorphic” sensors. We discuss estimation tasks with different priors for the signal. We find priors for which one sensor dominates the other and vice-versa, priors for which they are equivalent, and priors for which the answer depends on the power available. This shows that comparing sensors is a quite delicate problem. It also suggests that the optimal architecture might have more that one sensor, and would switch sensors on and off according to the performance level required instantaneously.
Starting Page	3319
Ending Page	3326
File Size	1393543
Page Count	8
File Format	PDF
e-ISBN	9781479969234
DOI	10.1109/ICRA.2015.7139657
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-05-26
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Bandwidth Robot sensing systems Noise Standards Image resolution Noise measurement
Content Type	Text
Resource Type	Article

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