Directionally Controlled Time-of-Flight Ranging for Mobile Sensing Platforms

Abstract

Abstract-Scanning time-of-flight (TOF) sensors obtain depth measurements by directing modulated light beams across a scene. We demonstrate that control of the directional scanning patterns can enable novel algorithms and applications. Our analysis occurs entirely in the angular domain and consists of two ideas. First, we show how to exploit the angular support of the light beam to improve reconstruction results. Second, we describe how to control the light beam direction in a way that maximizes a well-known information theoretic measure. Using these two ideas, we demonstrate novel applications such as adaptive TOF sensing, LIDAR zoom, LIDAR edge sensing for gradient-based reconstruction and energy efficient LIDAR scanning. Our contributions can apply equally to sensors using mechanical, optoelectronic or MEMS-based approaches to modulate the light beam, and we show results here on a MEMS mirror-based LIDAR system. In short, we describe new adaptive directionally controlled TOF sensing algorithms which can impact mobile sensing platforms such as robots, wearable devices and IoT nodes. Creating TOF sensors for personal drones, VR/AR glasses, IoT nodes and other miniature platforms would require transcending the energy constraints due to limited battery capacity. Recent work has addressed some aspects of TOF energy efficiency with novel illumination encodings. For example, by synchronizing illumination patterns to match sensor exposures [1], low-power reconstruction can occur for scenes with significant ambient light. Additionally, spatio-temporal encodings have been shown to be efficient for both structured light illumination [30] and TOF illumination as well [29]. I. INTRODUCTIONIn this paper, we demonstrate new efficiencies that are possible with angular control of a TOF sensor. We demonstrate this with a single LIDAR beam reflected off a microelectromechanical (MEMS) mirror. The voltages that control the MEMS actuators allow analog (continuous) TOF sensing angles. As a modulator, MEMS mirrors have well-known advantages of high-speed and fast response to control [32].

MEMS Mirrors for LiDAR: A Review

“…For instance, M. Saleem et al used a FoM combining the deflection angle, power consumption, and actuator temperature to optimize their electrothermal MEMS mirror design for endoscopic optical coherence tomography (OCT) applications [33]. U. Baran et al defined a FoM as the multiplication of the optical scanning angle, mirror width along the scanning direction, and resonant frequency of scanning mirrors [34] to compare piezoelectric MEMS mirrors for high-resolution displays. In this paper, a FoM is defined as the product of the scan angle, mirror area, and resonant frequency, and will be used to compare various MEMS mirrors for LiDAR applications.…”

Adaptive LiDAR Sampling and Depth Completion using Ensemble Variance

“…Recently, adaptive sampling algorithms for depth completion were introduced following the emergence of new optical machinery [38], [39] that allowed sampling irregular patterns more precisely. Wolff et al [4] proposed an image-driven sampling and reconstruction strategy based on dividing the image into approximately piecewise segments (using superpixels), followed by sampling each center of mass and filling the entire segment with the sampled depth.…”

Adaptive fovea for scanning depth sensors

“…This article is an extension of our conference paper (Tasneem et al, 2018) published at Robotics: Science and Systems in 2018. For this submission, we add the analysis of the design parameters for our adaptive sensing algorithm, demonstrate the advantages of our zooming algorithm over legacy TOF sensors on a robotic platform, release a wrapper in Gazebo to simulate the sensor, and present a foveated sensor model that allows for efficient robot motion in SLAM algorithms.…”