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The projected LiDAR points for our algorithm are located closer to the ground truth line than the projected points of the other methods. The performance of each algorithm is objectively evaluated by computing the line alignment error, as shown in Figure 8b. The average error of our method is 1.87 pixels. It is smaller than Wasielewski (2.7 pixels) and Li (5.5 pixels). We also evaluate the corresponding accuracy of each feature by measuring the point correspondence error (Figure 8c). In the case of Wasielewski and our method, the correspondence error of the center point is smaller than both the side points, which confirms our intuition that led to the weighted approach. Figure 8 The comparison of the extrinsic calibration algorithms proposed by Walsielewski, Li and us. To do this test, we used the four ground truth images we made. (a) Projection of the LiDAR points onto the two ground truth FARP1 images. The yellow band is the marked ... 8.1.3. Effect of the Number of Scan/Image Pairs We evaluated the line alignment accuracy as a function of the number of image/scan pairs. This evaluation was done using 10-fold cross-validation. The number of testing image/scan pairs was randomly selected from the 250 pairs. As shown in Figure 9, our result improves when using more image/scan pairs and achieves better performance with less data. The average error Obeticholic Acid in vitro of our method is 4.4 pixels. This error value is two-times lower than Wasielewski��s 8.5 pixels and 2.2-times lower than Li��s 9.6 pixels. When using 50 pairs, our approach has 43% better performance than Wasielewski��s algorithm using 100 pairs and 75% better than Li��s method with 100 pairs. This result means that our approach performs as well as the previous approaches with half of the data. It is interesting to note that the calibration accuracy of Li��s approach is not improved by increasing the number of pairs. Figure 9 Performance as a function of number of image and LiDAR scan pairs: we used a 10-fold cross-validation to evaluate the effect that the number of calibration data has on the calibration accuracy. Our approach was also affected by the number of calibration ... 8.1.4. Effect of the Range and Pose of the Calibration Target We determined the effect of the pose and range of the calibration target. To do this experiment, we first labeled image/scan pairs according to the range and pose of the calibration Doxorubicin mouse target. The range was divided into two categories: near range (