3.2 Prior Map Construction Test
To equip our robots with comprehensive environmental information, we
initially establish prior maps across the four scenarios in Fig. 3. This
vital stage paves the way for accurate navigation and path planning. The
resulting laser maps and point cloud maps are shown in Fig. 4.
The mapping results within crop rows are showcased in Fig. 4(a) and (e).
Both parallel crop rows and on-road obstructions are distinctly
depicted. Furthermore, even the detailed features of plant foliage on
both sides of the path are effectively represented. Fig. 4(b) and (f)
display the mapping results within a landscape greenhouse. The paths are
initially and clearly depicted. Following this, the precise details of
the plants bordering the paths are effectively mapped. These accurate
maps are crucial for navigation, as the determination of whether a path
is feasible relies on precise measurements of plant height. Fig. 4(e)
and (g) capture the mapping outcomes in complex greenhouse environments,
characterized by overlapping plants, irregular surfaces, and small
streams. The pathways are distinctly segmented, with even the uneven
road sections, indicated by the red circles in the figure, being well
represented. Despite a few obstructions, detailed features of the plants
alongside the pathways are also clearly depicted in the map. Lastly,
Fig. 4(d) and (h) reveal the mapping results of low-lying plants. The
brick-paved road and the plants on either side are accurately
reconstructed. As demonstrated in the figure, low-lying vegetation is
effectively represented. The muddy path between the plants, though
segmented, is less intuitively discernible.
Fig. 4 showcases the distinctive mapping capability of the improved
LeGO-LOAM algorithm in diverse greenhouse environments. The algorithm’s
robustness and high accuracy in large-scale complex greenhouses provide
a crucial basis for precision-oriented subsequent robotic navigation.