Low Power Depth Estimation of Rigid Objects for Time-of-Flight Imaging

TL;DR

This paper introduces a low-power depth estimation algorithm for TOF cameras that reduces power consumption by adaptively controlling the camera and estimating depth from images, suitable for battery-powered devices.

Contribution

The proposed method significantly decreases TOF camera usage and overall power consumption while maintaining accurate depth estimation on embedded platforms.

Findings

Reduces TOF camera usage by 85%

Achieves up to 73% total power savings

Maintains low error rate of 0.96% in depth estimation

Abstract

Depth sensing is useful in a variety of applications that range from augmented reality to robotics. Time-of-flight (TOF) cameras are appealing because they obtain dense depth measurements with minimal latency. However, for many battery-powered devices, the illumination source of a TOF camera is power hungry and can limit the battery life of the device. To address this issue, we present an algorithm that lowers the power for depth sensing by reducing the usage of the TOF camera and estimating depth maps using concurrently collected images. Our technique also adaptively controls the TOF camera and enables it when an accurate depth map cannot be estimated. To ensure that the overall system power for depth sensing is reduced, we design our algorithm to run on a low power embedded platform, where it outputs 640x480 depth maps at 30 frames per second. We evaluate our approach on several RGB-D datasets, where it produces depth maps with an overall mean relative error of 0.96% and reduces the usage of the TOF camera by 85%. When used with commercial TOF cameras, we estimate that our algorithm can lower the total power for depth sensing by up to 73%.