Gravity-constrained point cloud registration

Visual and lidar Simultaneous Localization and Mapping (SLAM) algorithms benefit from the Inertial Measurement Unit (IMU) modality. The high-rate inertial data complement the other lower-rate modalities. Moreover, in the absence of constant acceleration, the gravity vector makes two attitude angles out of three observable in the global coordinate frame. In visual odometry, this is already being used to reduce the 6-Degrees Of Freedom (DOF) pose estimation problem to 4-DOF. In lidar SLAM, the gravity measurements are often used as a penalty in the back-end global map optimization to prevent map deformations. In this work, we propose an Iterative Closest Point (ICP)-based front-end which exploits the observable DOF and provides pose estimates aligned with the gravity vector. We believe that this front-end has the potential to support the loop closure identification, thus speeding up convergences of global map optimizations. The presented approach has been extensively tested against accurate ground-truth localization in large-scale outdoor environments as well as in the Subterranean Challenge organized by Defense Advanced Research Projects Agency (DARPA). We show that it can reduce the localization drift by 30% when compared to the standard 6-DOF ICP. Moreover, the code is readily available to the community as a part of the libpointmatcher library.

Authors

• Vladimír Kubelka
• Maxime Vaidis
• François Pomerleau

Keywords

• Location awareness
• Visualization
• Simultaneous localization and mapping
• Laser radar
• Codes
• Libraries
• Calibration
• Point Cloud
• Point Cloud Registration
• Global Optimization
• Inertial Measurement Unit
• Pose Estimation
• Coordinate Frame
• Gravity Vector
• Global Frame
• Iterative Closest Point
• Loop Closure
• Visual Odometry
• Gravity Measurements
• Local Drift
• Global Positioning System
• Unmanned Aerial Vehicles
• Error Function
• Gravitational Force
• Corresponding Points
• Iterative Closest Point Algorithm
• Inertial Frame
• Underground Environment
• Factor Graph
• Pitch Angle
• LiDAR Scans
• Visual-inertial Odometry
• Underground Mining
• Roll Angle
• Tight Integrity