2019SPIE eBooksRequires access

Figures of Merit, Testing, and Calibration for LiDAR

Paul McManamon

Open publisher page 0 citations

Abstract

We need to be able to quantitatively measure the performance of a LiDAR. This will be more difficult than measuring the performance of a traditional 2D sensor because we have more dimensions to measure. The most common LiDAR is a 3D LiDAR, which involves measuring range response as well as angle/angle response. This chapter first discusses LiDAR figures of merit, which are the characteristics we need to measure to determine LiDAR performance (the discussion may not describe how to measure all of the figures of merit). Simple direct-detection LiDAR will not have as many relevant figures of merit as a coherent LiDAR. Next, the chapter discusses LiDAR testing, focusing primarily on 3D direct-detection LiDAR. Finally, to obtain optimum performance, we need to calibrate the LiDAR. The chapter concludes by providing methods to remove intensity and range measurement nonuniformities. Again, this discussion on calibration will focus on 3D directdetection LiDAR.

About this research paper

What this paper is about

We need to be able to quantitatively measure the performance of a LiDAR. This will be more difficult than measuring the performance of a traditional 2D sensor because we have more dimensions to measure. The most common LiDAR is a 3D LiDAR, which involves measuring range response as well as angle/angle response. This chapter first discusses LiDAR figures of merit, which are the characteristics we need to measure to determine LiDAR performance (the discussion may not describe how to measure all of the figures of merit). Simple direct-detection LiDAR will not have as many relevant figures of merit as a coherent LiDAR. Next, the chapter discusses LiDAR testing, focusing primarily on 3D direct-detection LiDAR. Finally, to obtain optimum performance, we need to calibrate the LiDAR. The chapter concludes by providing methods to remove intensity and range measurement nonuniformities. Again, this discussion on calibration will focus on 3D directdetection LiDAR.

Why it matters

A significance statement is not available in the OpenAlex record.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

We need to be able to quantitatively measure the performance of a LiDAR. This will be more difficult than measuring the performance of a traditional 2D sensor because we have more dimensions to measure. The most common LiDAR is a 3D LiDAR, which involves measuring range response as well as angle/angle response. This chapter first discusses LiDAR figures of merit, which are the characteristics we need to measure to determine LiDAR performance (the discussion may not describe how to measure all of the figures of merit). Simple direct-detection LiDAR will not have as many relevant figures of merit as a coherent LiDAR. Next, the chapter discusses LiDAR testing, focusing primarily on 3D direct-detection LiDAR. Finally, to obtain optimum performance, we need to calibrate the LiDAR. The chapter concludes by providing methods to remove intensity and range measurement nonuniformities. Again, this discussion on calibration will focus on 3D directdetection LiDAR.

Key concepts: Lidar, Measure (data warehouse), Remote sensing, Calibration, Figure of merit, Range (aeronautics), Environmental science, Computer science

Related papers

Back to paper searchBrowse research topicsOriginal source
Figures of Merit, Testing, and Calibration for LiDAR — Research Paper | ScholarLens