Fast Semi-Direct Monocular Visual Odometry SVO Fast Semi-Direct Monocular Visual Odometry Christian Forster, Matia Pizzoli, Davide Scaramuzza Shervin Ghasemlou – November 2015

Introduction SVO: Is a method that combines Feature based methods and direct methods for visual Odometry According to authors, All Visual Odometry works for MAVs are featurebased. SVO is more robust and allows faster flight maneuvers Higher accuracy and speed Shervin Ghasemlou – December 2015

Introduction Feature based Methods: Extract a sparse set of features Match them in successive frames Recover camera pose and also structure using epipolar geometry Finally refine pose and structure Shervin Ghasemlou – December 2015

Introduction Direct Methods: Uses intensity of the image Exploit information from all parts of the image These methods outperform feature based mthods in term of robustness In scenes with little textures Camera defocus Motion blur They save time of feature detection Shervin Ghasemlou – December 2015

Contributions 1-A novel semi direct VO method for MAVs, which in comparison with the state of the art methods, is faster More accurate 2-Integration of a probabilistic mapping method Robust to outliers Shervin Ghasemlou – December 2015

Algorithm Two Thread Motion Estimation Mapping Sparse model-based image alignment Feature alignment Pose and structure refinement Mapping Shervin Ghasemlou – December 2015

Flowchart Shervin Ghasemlou – December 2015

Motion Estimation (1)Sparse mode based image alignment The maximum likelihood estimate of the rigid body transformation Tk,k−1 between two consecutive camera poses minimizes the negative log-likelihood of the intensity residuals: The intensity residual dδ is defined by the photometric difference between pixels observing the same 3D point. Shervin Ghasemlou – December 2015

Motion Estimation It can be computed by back-projecting a 2D point u from the previous image Ik−1 and subsequently projecting it into the current camera view: Where Shervin Ghasemlou – December 2015

Motion Estimation Shervin Ghasemlou – December 2015

Motion Estimation (2)Relaxation through feature alignment The last step aligned the camera with respect to the previous frame To reduce the drift, the camera pose should be aligned with respect to the map,rather than to the previous frame Shervin Ghasemlou – December 2015

Motion Estimation Shervin Ghasemlou – December 2015

Motion Estimation (3)Pose and Structure Refinement In this final step, we again optimize the camera pose Tk,w to minimize the re-projection residuals: Shervin Ghasemlou – December 2015

Motion Estimation Shervin Ghasemlou – December 2015

Experimental results Experiments were performed on a data set recorded from two sources On the quad-rotor A handheld camera Process done on : Quad-copter embedded platform A laptop Two settings One for accuracy One for speed Shervin Ghasemlou – December 2015

Experimental results Comparison with PTAM The proposed method has fewer outliers due to the depth-filter Shervin Ghasemlou – December 2015

Questions?