Algorithms for Smoothing of Individual Line Features

2002 ◽  
Vol 32 (4) ◽  
pp. 751-756 ◽  
Author(s):  
Christoph Kleinn ◽  
Berthold Traub ◽  
Christian Hoffmann

Length of line features, such as forest border, is among the ecologically interesting attributes estimated from forest inventories. In hilly terrain, observed line lengths must be corrected for slope. Contrary to the correction for standard area-related attributes (like volume per hectare), an overall correction of plot size is not sufficient, but the actual inclination of each individual line segment must be used for slope correction. This topic is discussed, and a mean correction factor is calculated as a function of terrain inclination assuming a uniform angular distribution of lines on the slope. Furthermore, the question is discussed whether the standard slope correction procedure for fixed-area circular field plots may possibly introduce a systematic error into the estimation of line length and also of standard area-related attributes. It is concluded that no relevant error is to be expected, neither with respect to point estimates nor to interval estimates. Data from the second Swiss National Forest Inventory serves for illustration.


1998 ◽  
Vol 500 (2) ◽  
pp. 1069-1069 ◽  
Author(s):  
Y. Ueda ◽  
H. Inoue ◽  
Y. Tanaka ◽  
K. Ebisawa ◽  
F. Nagase ◽  
...  
Keyword(s):  
X Ray ◽  

2021 ◽  
Vol 92 (4) ◽  
pp. 043512 ◽  
Author(s):  
E. P. Hartouni ◽  
R. M. Bionta ◽  
D. T. Casey ◽  
M. J. Eckart ◽  
M. Gatu-Johnson ◽  
...  

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1196
Author(s):  
Gang Li ◽  
Yawen Zeng ◽  
Huilan Huang ◽  
Shaojian Song ◽  
Bin Liu ◽  
...  

The traditional simultaneous localization and mapping (SLAM) system uses static points of the environment as features for real-time localization and mapping. When there are few available point features, the system is difficult to implement. A feasible solution is to introduce line features. In complex scenarios containing rich line segments, the description of line segments is not strongly differentiated, which can lead to incorrect association of line segment data, thus introducing errors into the system and aggravating the cumulative error of the system. To address this problem, a point-line stereo visual SLAM system incorporating semantic invariants is proposed in this paper. This system improves the accuracy of line feature matching by fusing line features with image semantic invariant information. When defining the error function, the semantic invariant is fused with the reprojection error function, and the semantic constraint is applied to reduce the cumulative error of the poses in the long-term tracking process. Experiments on the Office sequence of the TartanAir dataset and the KITTI dataset show that this system improves the matching accuracy of line features and suppresses the cumulative error of the SLAM system to some extent, and the mean relative pose error (RPE) is 1.38 and 0.0593 m, respectively.


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