![](bga.png)
SLAM2 are significantly larger than the ones repor-
ted by FDMO as Fig. 5 also highlights. On the other
hand, DSO tracking failed entirely on various occa-
sions, and when it did not fail, it reported a signifi-
cantly large increase in drifts over the second loop. As
DSO went through the transition frames between the
loops, its motion model estimate was violated, errone-
ously initializing its highly non-convex tracking op-
timization. The optimization got subsequently stuck
in a local minimum, which led to a wrong pose esti-
mate. The wrong pose estimate was in turn used to
propagate the map, thereby causing large drifts. On
the other hand, FDMO was successfully capable of
handling such a scenario, reporting an average impro-
vement of 51%, 61% and 7 % in positional, rotatio-
nal, and scale drifts respectively, when compared to
the best of both DSO and ORB-SLAM2, on most se-
quences.
The results reported in the second experiment
(Fig. 6) quantify the robustness limits of each sy-
stem to erratic motions. Various factors may affect the
obtained results, therefore, we attempted the experi-
ments under various types of motion and by skipping
frames towards a previously observed (herein referred
to as backward) and previously unobserved part of the
scene (referred to as forward). The observed depth of
the scene is also an important factor: far-away sce-
nes remain for a longer time in the field of view, thus
improving the systems’ performance. However, we
cannot model all different possibilities of depth vari-
ations; therefore, for the sake of comparison, all sy-
stems were subjected to the same frame drops at the
same locations in each experiment where the obser-
ved scene’s depth varied from three to eight meters.
The reported results highlight DSO’s brittleness to
any violation of its motion model; where translations
as little as thirty centimeters and rotations as small
as three degrees introduced errors of over 50% in its
pose estimates. On the other hand, FDMO was ca-
pable of accurately handling baselines as large as 1.5
meters and 20 degrees towards previously unobser-
ved scene, after which failure occurred due to feature-
deprivation, and two meters toward previously obser-
ved parts of the scene. ORB-SLAM2’s performance
was very similar to FDMO in forward jumps, howe-
ver it significantly outperformed it by twice as much
in the backward jumps; ORB-SLAM2 uses a global
map for failure recovery whereas FDMO, being an
odometry system, can only make use of its immediate
surroundings. Nevertheless FDMO’s current limitati-
ons in this regard are purely due to our current imple-
mentation as there are no theoretical limitations of de-
veloping FDMO into a full SLAM system. However,
using a global relocalization method has its downside;
the jitter in ORB-SLAM2’s behaviour (shown in Fig.
6 (C)) is due to its relocalization process erroneously
localizing the frame at spurious locations. Another
key aspect of FDMO, visible in this experiment, is
its ability to detect failure and not incorporate it into
its map. In contrast, toward their failure limits, both
DSO and ORB-SLAM2 incorporate spurious measu-
rements for few frames before failing completely.
6 CONCLUSION
This paper successfully demonstrated the advantages
of integrating direct and feature-based methods in
VO. By relying on a feature-based map when direct
tracking fails, the issue of large baselines that is cha-
racteristic of direct methods is mitigated, while main-
taining the high accuracy and robustness to feature-
deprived environments of direct methods in both
feature-based and direct maps, at a relatively low
computational cost. Both qualitative and quantitative
experimental results proved the effectiveness of the
collaboration between direct and feature-based met-
hods in the localization part.
While these results are exciting, they do not make
use of a global feature-based map; as such we are
currently developing a more elaborate integration be-
tween both frameworks, to further improve the map-
ping accuracy and efficiency. Furthermore, we antici-
pate that the benefits to the mapping thread will also
lead to added robustness and accuracy to the motion
estimation within a full SLAM framework.
ACKNOWLEDGEMENTS
This work was funded by the University Research
Board (UBR) at the American University of Beirut,
and the Canadian National Science Research Council
(NSERC).
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