3.3.2 Analysis of Localization Robustness
Fig. 5(a) displays the Absolute Pose Error (APE) of the algorithm before
and after improvements in scenario 1. With specific numerical data from
Table 3, significant improvements can be observed in all error metrics
of APE. For example, the Root Mean Square Error (RMSE) decreased from
1.7565 before improvement to 0.3959 afterwards, representing an
improvement of approximately 77%. The Maximum Error (Max) also
decreased from 2.7385 to 0.7843, a reduction of over 70%, indicating
that the improved algorithm can more effectively control the maximum
pose error. Additionally, the Mean Error decreased from 1.6543 to
0.3630, a drop of around 78%, demonstrating a significant reduction in
the average error. These data suggest that incorporating the
relocalization module has notably increased the global pose estimation
accuracy of Lidar SLAM. This test indicates that our relocalization
module can significantly reduce the positioning error in crop row
scenes.
Fig. 5(b) presents the test results in a flat greenhouse environment. By
observing the data in Table 3, it can be observed that the improved
algorithm shows some degree of improvement in APE. For example, the RMSE
decreased by approximately 20%, from 4.3098 to 3.4303. The Mean Error
also reduced from 3.9928 to 3.0176, representing a decrease of around
24%. However, the level of improvement in other key metrics is
relatively small. The Maximum Error only decreased by about 4%, and the
Standard Deviation (Std) did not change significantly. On the other
hand, a slight increase in Std may suggest that under certain
conditions, such as specific noise or environmental changes, the
improved algorithm may still have areas that require further
improvement. From this test, it can be concluded that our relocalization
module can generally reduce the positioning error in a relatively simple
environment. However, the effects may not be as pronounced as in more
complex environments. This could be attributed to the smaller pose
changes between consecutive frames in simpler environments, where even
minor errors can potentially impact the stability of APE.
Fig. 5(c) presents the test results from scenario 3. When comparing the
algorithms before and after enhancement, a significant improvement in
APE is evident, as shown in Table 3. Particularly, the RMSE value
decreased from 4.4524 to 2.3813, representing a reduction of over 46%.
These demonstrate the effectiveness of our relocation algorithm in an
environment with various terrains and interference factors. From this
test, it can be concluded that our relocalization module has excellent
effects in reducing global positioning errors in complex scenarios.
Fig. 5(d) showcases the test results from scenario 4, which has many
low-lying plants. Referring to Table 3 and comparing the algorithms
before and after enhancement in terms of APE, it is evident that almost
all metrics of the enhanced algorithm have significantly improved. For
example, the RMSE dropped from 3.4979 to 2.3496, representing a decrease
of about 33%. This indicates that our relocation module can still
provide significant optimization in scenes with dirt roads and
interference from low-lying plants.
These results indicate that our improved algorithm demonstrates notable
efficacy across various environments. The relocalization module
consistently reduces the APE in all experimental scenarios, leading to
an enhancement in the accuracy of global pose estimation. This improved
performance is particularly noticeable in complex environments with
rugged roads or low-lying plant interference. Therefore, the Improved
LeGO-LOAM algorithm in this study is capable of providing precise
localization information for adaptive navigation within a greenhouse.
Table 3. Comparisons of the APE values between the Two Methods.