AVENUE: Visual Localization Results
Fig.1: Database of models
To perform the visual localization experiment, we created an accurate
model database of various buildings throughout Columbia University
Morningside campus (Fig.1). The model was created by scanning prominent
features with an electronic theodolite (a.k.a. total station).
The features modeled were windows, ledges and linear decorations --- all
commonly found and abundant in urban landscapes.
Fig.2: Experimental locations
Fig.3: Localization images
To test the accuracy of the visual localization method, a total
of 16 tests were performed at various locations throughout the
campus. The robot was driven manually along a long trajectory,
stopped at each of the test locations and directed to perform
the visual localization routine. It used the odometry pose
estimates as an initial guess to determine the nearby buildings
and choose which model to use.
Figure 2 in the side panel shows a 2-D map of the campus. The locations
of the tests are marked with black dots and the orientations of the cameras
at these locations --- with arrows. The yellow and red colors outline
buildings and the green color outlines vegetation.
Figure 3 is a table that illustrates visually the results for each of the
test locations. Each row corresponds to the test at the location number shown
in the first column. The left image in the row illustrates the initial pose
guess by overlaying the model of the building on the image taken by the camera.
The discrepancy between the projection of the model and the image
illustrates the innaccuracy of the initial guess. The image to the right
shows the same model projected onto the same image after the visual pose
estimation step. You can see that in all cases the alignment is very
good. The last column shows the error of the position estimate compared
to ground truth data.
Fig.4: Consistency experiment 1
Fig.5: Consistency experiment 2
The goal of these experiments was to verify that the visual localization
algorighm produces consistent results. Two such experiments were performed
in two different locations. In each of these locations, a pair of images
were taken of different building facades by only turning the camera without
changing its position significantly. The visual localization algorithm was
performed on both images and the position results were compared.
The figures linked to from the side panel illustrate the results visually in the
form of a table. The first row of the table is related to the first image of the
pair and the second table row refers to the second image. The first column shows
the image with the corresponding model overlaid using the initial pose estimate
(i.e. the before image), while the second column illustrates the computed
pose of the camera starting from the initial guess (i.e. the after image).
For consistency experiment 1, the estimate inconsistency between the two images was
0.064m (Fig.4). For consistency experiment 2, the inconsistency was 0.290m (Fig.5).
Intergrated system test
Finally, an experiment was performed to confirm that the entire robot
localization system works well together, i.e.
it uses the
visual localization method as needed and that it actually improves the
performance. A more than 330m long trajectory was composed on the
campus. The robot was directed to follow autonomously the trajectory
using all sensors and choosing the localization method as needed.
Fig.6: Map of integrated test
During the test run, the robot passed through both areas of good GPS
coverage and poor GPS coverage (Fig.6). It was able to consistently
detect the areas of poor GPS performance (marked in the figure) and
employ the visual method to improve its pose estimation accuracy.
Notice that no GPS data was available at all at location 3, as the
robot was directly beneath an extension of the nearby building.
The table below shows the position errors of the open space
localization method and the visual localization method at
each of these locations. It clearly illustrates the improvement
the visual localization method brings.
||Open space method
|1. ||1.297m ||0.348m|
|2. ||1.031m ||0.345m|
|3. ||0.937m ||0.179m|
|4. ||1.212m ||0.274m|
The experiments above show that accurate mobile robot localization in
urban environment is possible. The first set of experiments shows an
average error of 0.261m, which is definitely acceptable for the task
at hand. The second set of experiments confirms that the results are
consistent to a high degree regardless of the building chosen to
perform the visual experiment on. Finally, the last experiment verified
in practice the feasibility of autonomous navigation in urban terrain
using the combination of open-space and visual localization methods
as well as the improved performance of the combined system over using
only GPS and inertial sensors.