Abstract

We propose an expectation maximization (EM) strategy for improving the precision of time of flight (ToF) light detection and ranging (LiDAR) scanners. The novel algorithm statistically accounts not only for the bias induced by temperature changes in the laser diode, but also for the multi-modality of the measurement noises that is induced by mode-hopping effects. Instrumental to the proposed EM algorithm, we also describe a general thermal dynamics model that can be learned either from just input-output data or from a combination of simple temperature experiments and information from the laser’s datasheet. We test the strategy on a SICK LMS 200 device and improve its average absolute error by a factor of three.

Highlights

  • time of flight (ToF) light detection and ranging (LiDAR) estimate distances by emitting short bursts of laser light and by measuring the time it takes for the reflected photons to arrive back to the device [1]

  • ToF LiDARs estimate distances by emitting short bursts of laser light and by measuring the time it takes for the reflected photons to arrive back to the device [1]

  • Physical considerations on the mode-hopping effect lead to the consideration that the measurement noise of time of flight (ToF) laser scanners is intrinsically multi-modal

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Summary

Introduction

ToF LiDARs estimate distances by emitting short bursts of laser light and by measuring the time it takes for the reflected photons to arrive back to the device [1]. Despite being based on a very simple principle, they are very much both accurate and precise devices [2]: for example, precisions can reach 10 mm of standard error when the object is 10 m away. Due to these favorable properties, they are commonly used in critical industrial applications where there is the need for high quality measurements. Manufacturers of ToF devices usually embed opportune algorithms in their products that implement this temperature compensation mechanism

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