We propose a framework for bundle adjustment (BA) on sparse lidar points and incorporate it to a lidar odometry and mapping (LOAM) to lower the drift. A local BA on a sliding window of keyframes has been widely used in visual SLAM and has proved to be very effective in lowering the drift. But in lidar mapping , BA method is hardly used because the sparse feature points (e.g., edge and plane) in a lidar point-cloud make the exact feature matching impossible. Our method is to enforce feature points lie on the same edge or plane by minimizing the eigenvalue of the covariance matrix. To speedup the optimization, we derive the analytical derivatives  , up to second order, in closed form. Moreoever , we propose a novel adaptive voxelization method to search feature correspondence efficiently. The proposed formulations are incorporated into a LOAM back-end for map refinement. Results show that, although as a back-end, the local BA can be solved very efficiently, even in real-time at 10Hz when optimizing 20 scans of point-cloud. The local BA also considerably lowers the LOAM drift. Our implementation of the BA optimization and LOAM are open-sourced to benefit the community.
A local Bundle Adjustment (BA) on a sliding window of keyframes has been widely used in visual SLAM and proved to be very effective in lowering the drift. But in lidar SLAM , BA method is hardly used because the sparse feature points (e.g., edge and plane) make the exact point matching impossible. In this paper, we formulate the lidar BA as minimizing the distance from a feature point to its matched edge or plane . Unlike the visual SLAM (and prior plane adjustment method in lidar SLAM) where the feature has to be co-determined along with the pose, we show that the feature can be analytically solved and removed from the BA, the resultant BA is only dependent on the scan poses. This greatly reduces the optimization scale and allows large-scale dense plane and edge features to be used. To speedup the optimization, we derive the analytical derivatives of the cost function , up to second order, in closed form. Moreover , we propose a novel adaptive voxelization method to search feature correspondence efficiently. The proposed formulations are incorporated into a LOAM back-end for map refinement. Results show that, although as a back-end, the local BA can be solved very efficiently, even in real-time at 10Hz when optimizing 20 scans of point-cloud. The local BA also considerably lowers the LOAM drift. Our implementation of the BA optimization and LOAM are open-sourced to benefit the community.
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We propose a framework for bundle adjustment
A local Bundle Adjustment
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sparse lidar points and incorporate it to a lidar odometry and mapping (LOAM) to lower the drift. A local BA on a
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mapping
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in a lidar point-cloud
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Our method is to enforce feature points lie on the same
In this paper, we formulate the lidar BA as minimizing the distance from a feature point to its matched
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by minimizing the eigenvalue of the covariance matrix.
. Unlike the visual SLAM (and prior plane adjustment method in lidar SLAM) where the feature has to be co-determined along with the pose, we show that the feature can be analytically solved and removed from the BA, the resultant BA is only dependent on the scan poses. This greatly reduces the optimization scale and allows large-scale dense plane and edge features to be used.
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of the cost function
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