The Interpretation of Phase and Intensity Data from AMCW Light Detection Sensors for Reliable Ranging

Abstract

The analysis of sensor range data and its application to mobile robot navigation are of crucial importance in the field of mobile robotic research.We analyze the range data produced by an amplitudemodulated continuous wave (AMCW) light detection and ranging sensor and show that by physically modeling such sensors, we not only can produce reliable range estimates, but can also quantify our certainty in each range data point. We discuss the noise in the sensor and show the importance of using both phase and intensity data for calibration and data interpretation.We consider in detail the phenomenon of "mixed pixel points" whereby false range measurements occur when the light beam transmitted is split between two or more surfaces of differing range and/or reflectivity. We describe a new algorithm capable of detecting sudden changes in surface reflectance and/or range in order to identify these "spurious" data points. We quantify the regions over which the detection method will work, as we consider its sensitivity to changes in range and surface reflectance, while also quantifying the possibility of falsely predicting a discontinuity.

Correction of Erroneous LiDAR Measurements in Artificial Forest Canopy Experimental Setups

“…The impact of object reflectance on ghost points is verified, as in [18,20,21]. The effect on gpr if scanning resolution increases is comparable to the effect on gpr if objects are larger.…”

“…Previous research has focused on understanding the sources of error in the sensing and modeling process [18][19][20]. Similarly, other studies have aimed to characterize laser instruments and analyze the effect of various operating parameters in order to identify edges and remove the unwanted data points by means of e.g., two-dimensional edge-detection processes [21], and algorithms to detect depth discontinuity and mixed pixels in 3D data [20,22]. A manual selection and correction of the point cloud during the TLS data preprocessing procedure has also been tested [9], but this technique is not efficient to delete ghost points on large datasets (e.g., real forest canopies).…”

“…This is especially important in forest canopy research, since ghost points represent true interceptions of the laser beam, therefore, they should be considered in gap probability analysis (e.g., leaf area index, canopy cover), but have to be filtered out for structural analysis (e.g., study of single stem shape) [7]. In addition, since sensitivity of edge detection algorithms decreases with increasing range, more accurate results can be obtained by selecting adaptive threshold functions, so that within the ranges of interest the filter could be as sensitive as possible [21].…”

A Comparative Analysis of Depth-Discontinuity and Mixed-Pixel Detection Algorithms

“…Adams [8,12] developed computational models of the mixed pixel effect for amplitude modulated continuous wave (AMCW) scanners. The models, however, require the capability of obtaining many overlapping samples of range and signal strength as the laser beam passes across a depth discontinuity.…”

Weighted range sensor matching algorithms for mobile robot displacement estimation

“…1). While the existence of these sources of uncertainty has previously been suggested [11,12,7,10], our algorithm is the first to model their effect and account for it within the estimation process. Finally, by explicitly incorporating these models of uncertainty, our algorithm computes a realistic covariance estimate that accurately reflects the true uncertainty in the displacement estimates.…”