Integrating LIDAR into Stereo for Fast and Improved Disparity Computation
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1 Integrating LIDAR into Stereo for Fast and Improved Computation Hernán Badino, Daniel Huber, and Takeo Kanade Robotics Institute, Carnegie Mellon University Pittsburgh, PA, USA
2 Stereo/LIDAR Integration The appropriate stereo/lidar fusion compensates for individual sensor deficiencies Stereo: dense but noisy at large distances LIDAR: sparse but accurate Stereo + LIDAR: dense and accurate Stereo can produce large number of false positives leading to phantom objects Problems: lack of texture, depth discontinuities, and repetitive patterns Solution: improve stereo estimation before fusion occurs The Robotics Institute, Carnegie Mellon University Slide Nr
3 Stereo Computation Ranges Taxonomy of Stereo Vision Methods: (D. Scharstein and R. Szeliski) Expected Range and Normals Matching Cost Computation Cost Aggregation Comp / Opt. Refinement Matching Cost Volume Space Image (WxHxD) Raw disparity map (WxH) Final disparity map (WxH) AD, SD, MI, Census, etc. Fixed or variable windows, multiple linear paths, tree paths, etc. WTA, Dynamic Programming, Belief Propagation, Graph Cut, etc. Sub-pixel interpolation, occlusion detection, consistency checks, etc. The Robotics Institute, Carnegie Mellon University Slide Nr
4 Space Image ( u, v, d) DSI ( u, v, d) = L( u, v) R( u + d, v) H The disparity range D defines the depth range of observability D W The Robotics Institute, Carnegie Mellon University Slide Nr
5 Space Image H W The Robotics Institute, Carnegie Mellon University Slide Nr D
6 Reduced Space Image H D W The Robotics Institute, Carnegie Mellon University Slide Nr 6
7 Reduction of the Range Space.... Calculate Spherical Range Image Apply Min/Max filter Predict Min/Max Images Calculate reduced DSI H D W The Robotics Institute, Carnegie Mellon University Slide Nr 7
8 Comparison of results with WTA Left Image SRI Reduced DSI Standard DSI The Robotics Institute, Carnegie Mellon University Slide Nr 8 8
9 Stereo Computation Taxonomy of Stereo Vision Methods: (D. Scharstein and R. Szeliski) Range Expected and Gradient Matching Cost Computation Cost Aggregation Comp / Opt. Refinement Matching Cost Volume Space Image (WxHxD) Raw disparity map (WxH) Final disparity map (WxH) AD, SD, MI, Census, etc. Fixed or variable windows, multiple linear paths, tree paths, etc. WTA, Dynamic Programming, Belief Propagation, Graph Cut, etc. Sub-pixel interpolation, occlusion detection, consistency checks, etc. The Robotics Institute, Carnegie Mellon University Slide Nr 9
10 Space Image H W The Robotics Institute, Carnegie Mellon University Slide Nr 0 D
11 Space Image H W The Robotics Institute, Carnegie Mellon University Slide Nr D
12 Dynamic Programming S E Column The Robotics Institute, Carnegie Mellon University Slide Nr
13 Data Term S E Column The Robotics Institute, Carnegie Mellon University Slide Nr
14 Smoothness Term S E Column The Robotics Institute, Carnegie Mellon University Slide Nr
15 Smoothness Term S E Column The Robotics Institute, Carnegie Mellon University Slide Nr
16 Smoothness Term S E Column The Robotics Institute, Carnegie Mellon University Slide Nr 6
17 Optimal Solution S E Column The Robotics Institute, Carnegie Mellon University Slide Nr 7
18 Path Promotion S E Column The Robotics Institute, Carnegie Mellon University Slide Nr 8
19 Path Promotion S E Column The Robotics Institute, Carnegie Mellon University Slide Nr 9
20 Path Promotion S E Column The Robotics Institute, Carnegie Mellon University Slide Nr 0
21 Path Promotion S E Column The Robotics Institute, Carnegie Mellon University Slide Nr
22 Path Promotion S E Column The Robotics Institute, Carnegie Mellon University Slide Nr
23 Improvement Achieved The Robotics Institute, Carnegie Mellon University Slide Nr
24 Results The Robotics Institute, Carnegie Mellon University Slide Nr
25 Results The Robotics Institute, Carnegie Mellon University Slide Nr
26 Conclusions DSI reduction leads not only to a improved disparity computation but also reduces the computational complexity (0-0%). LIDAR ranges can be naturally integrated into the optimization algorithm by promoting paths and path directions in disparity space. Early integration of LIDAR range data into the stereo algorithm leads to a substantial improvement of the disparity image The Robotics Institute, Carnegie Mellon University Slide Nr 6
27 Thanks for your attention The Robotics Institute, Carnegie Mellon University Slide Nr 7
Integrating LIDAR into Stereo for Fast and Improved Disparity Computation
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