while inadequate matches result in a large

The error

metric

depends on the final geometric configuration

of both maps and the number of points in the lidar

stem center map.

The algorithm does not always guarantee better results than

the standard

ICP

method. In particular, the algorithm may not

perform better in situations where there are moderate outliers

present in the data. However, the algorithm will not provide

worse results than standard

ICP

, as it will default to the stan-

dard approach if distance error

cannot be improved further.

In addition, as seen in Figure 7 and Figure 8, stems ob-

served in overhead imagery are generally more than those

observed on the ground by the lidar. This is mainly due to, (a)

the presence of dense underbrush and other trees that obscure

the line of the sight of the lidar, and (b) the lidar has a limited

measurement range. These factors lead to lower number of

trees that are observed on the ground compared to overhead

imagery. This is a realistic constraint that was taken into ac-

count while developing the algorithm.

System Test and Simulation Setup

Without access to a real rover, and to simulate the envisaged op-

erational scenario, a ground vehicle was simulated in Matlab tra-

versing the 4-sided polygonal path and using the acquired over-

head imagery and lidar data as input. The simulated rover path is

illustrated as shown in Figure 5. More specifically, the acquired

lidar data from the Lake Mize site was gridded and incrementally

fed at each pose to the vision based localization algorithm.

It is noted that the rover path is composed of 234 poses

that are spaced at 0.5 m intervals. At each pose, lidar data is

sliced in 35 m × 35 m squares centered on each simulated

pose of the rover. These slices are used when the matching

algorithm is invoked and are fed to the lidar tree stem identi-

fication and extraction algorithm, which in turn detects and

labels tree stem centers for use by the matching algorithm.

Since the lidar dataset is of high accuracy, it was treated as

the ground reference to which geoposition estimates from the

localization algorithm are compared against. More specifically,

for our testing purposes, the algorithm’s estimated rover posi-

tion is graphed with the corresponding

GPS

location, which is

derived from the overlay of the simulated rover path onto the

lidar data. To assess the accuracy of position estimates, the

horizontal position error between the result provided by the

localization algorithm and the

GPS

estimate is calculated.

Overall Accuracy Results and Assessment

Ten full tests were performed that included processing and

labeling of input data by the localization algorithm in an end-

to-end fashion. The tests were differentiated by the geometry

of the path taken by the rover in simulation. In essence, due

to the different geometry of each path, slicing of the lidar data

was different and hence the paths would differ in the number

and geometry of stems observed from pose to pose. Figure 9

and Figure 10 show two examples of different paths taken

and the results from running the vision-based localization al-

gorithm. The error between the estimated and actual positions

at each pose was defined by:

= −

(

)

+ −

(

)

2

2

(3)

where

and

are the absolute (true) easting and north-

ing coordinates of the rover at pose

, and

and

are the

algorithm’s estimated easting and northing coordinates of the

rover at pose

.

The mean position error, standard deviation, and Root

Mean Squared (

RMS

) error for all test runs are shown in Figure

11. The mean rover position error for all test runs agrees to

Figure 9. Result from Path 1 Simulation.

Figure 10. Result from Path 6 Simulation.

Figure 11. Simulation Results per Path.

PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING

845

SEO Version