Realtime Visual and Point Cloud SLAM Nicola Fioraio, Kurt Konolige

1. Realtime Visual and Point Cloud SLAM Nicola Fioraio, Kurt Konolige

2. Real-Time Visual and Point Cloud SLAM • Goal: • Integrate ICP and visual features in frame-frame matching • Perform global optimization over all frames and features • Do it in real time • Techniques for real-time ICP: • Subsample on a regular grid (~1000 points) • Fast matching via reprojection • NLSQ constraints, using the cost function ; emulates Segal et al. “Generalized-ICP” (RSS 2009) • is the distance between matching points • defines the non-isotropic error for point-point, point-plane and plane-plane match • Techniques for Bundle Adjustment: • Both ICP and visual features are NLSQ constraints, so they can be used together • Global optimization is Bundle Adjustment over all ICP matches and visual features • Henry et al. “Rgb-d mapping” (ISER 2010) use reduction to pose graph, no features • Endres et al. “RGBD-SLAM” also reduces to pose-pose constraints Realtime performance for the pairwise alignment Global BA: 77899 edges => 346ms • Techniques for Bundle Adjustment: • Both ICP and visual features are NLSQ constraints, so they can be used together • Global optimization is Bundle Adjustment over all ICP matches and visual features • Henry et al. “Rgb-d mapping” (ISER 2010) use reduction to pose graph, no features • Endres et al. “RGBD-SLAM” also reduces to pose-pose constraints