Graphs
shadow1runner opened this issue · 10 comments
- Difference between Kalman-filtered FOE, raw FOE, and Kalman-filtered FOE with descend detection.
- compare to Kalman with Sebastian (which used a fitted polynomial, even though the code didn't show any of these signs)
# OBSOLETE -> see below
- Table: System performance
as well as graph analogous to Fig. 9 in
@inproceedings{al2016monocular,
title={Monocular vision-based obstacle detection/avoidance for unmanned aerial vehicles},
author={Al-Kaff, Abdulla and Meng, Qinggang and Mart{'\i}n, David and de la Escalera, Arturo and Armingol, Jos{'e} Mar{'\i}a},
booktitle={2016 IEEE Intelligent Vehicles Symposium (IV)},
pages={92--97},
year={2016},
organization={IEEE}
}
# DONE (graph only)
- ground obstacle detection
- descend detection?
# DONE
- difference between distortion enabled/disabled, a good example is the balconyCrash (
/home/helli/d/m/qgroundcontrol/src/CollisionAvoidance/opticalflow/res/boscam/fisheye/longFisheye/90degDown/balconyCrash.avi
) -> also include the optical flow field generated which shows the radial nature of the non-undistorted frame
# DONE
- Impact of CollisionLevel degradation over time
# DONE
- Comparison of KF (cf. #55) to Sebastian's settings, compare FOE movement and discuss
Proposal: implement two KF instances and let both do their magic - compare this movement, easier than merging Scala and C++
Branch graph/kf
and QGroundControl_kalman.ini
shows results have been drawn, it would be best to include 1e5/kalman_balconyCrash.pdf
shows ok-ish values which can be used.
# DONE
- Difference between divergence obtained by
- an affine model
- the 'differential' method
in both time and value, cf.Figure 8.8
&8.9
in Sebastian's thesis
# PROGRESS: There's hardly any correlation between those two methods (0.33 in figures), the affine model showed better results wrt. almost all unit tests - even though the divergence thresholds are fundamentally different (as they don't correlate to the differential method as mentioned before)
#TODO: Redo performance measurements with new affine model
- Influence of rotation in the evaluation (gimbal etc.)
- bottom-mounted static mounted camera and comparisons with this footage?
-> think about a meaningful possibility to compare these two methods, maybe also compare it to Stabiner, 2014
- lateral collision detection
- determine optimal number of vectors in affine differgence wrt.
- accuracy (in comparison to use all vectors in a patch)
- performance (the less the better, I assume?)