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P H O T O B U N D L E This is the source code for H. Alismail, B. Browning, and S. Lucey, ``Photometric Bundle Adjustment for Vision-Based SLAM,'' ACCV 2016 See additional information here as well: https://www.cs.cmu.edu/~halismai/h_alismail_robotics_2016.pdf INSTALLATION The code is tested on Linux (Debian, g++-4.9 and kernel 3.16.0-4-amd64) Required packages: - opencv - ceres - eigen3 - boost (program_options and filesystem) - TBB [optional] USAGE See the example code in apps/run_kitti.cc The code is meant to be used as refinement on initial pose results obtained with a visual odometry, or VSLAM system. IMPORTANT: pose initialization is the *local* frame--frame pose. The code returns the refined poses in the *global/cumulative* coordinate frame. TODO The code is a research base and is very rough around the edges. It currently does not run in real-time, but there are various possible improvements that should improve the speed, which include: - Pyramid implementation - Refactoring the DescriptorError function to create a CostFunction per image instead of per point. The current implementation (an objective per point) is very inefficient due to thrashing the cache - In addition to refactoring, implementing the CostFunction with an analytic Jacobian should significantly improve performance. The current implementation relies on ceres' automatic differentiation facilities which limit any possibilities of exploiting SIMD instructions for a more efficient implementation (the bottleneck is in patch interpolation) There are also opportunities for improving the accuracy. These include: - Proper selection of the characteristic scale of the Loss function - Better pixel selection: - By selecting the smallest number of points sufficient to produce pose estimates robustly - By selecting the *best* points to constrain the solution If you have questions/thoughts please contact me Thu Dec 1 00:13:44 EST 2016 Hatem Alismail <hatem.alismail @ gmail>