上海科技大学知识管理系统

Purely vision-based localization and mapping is a cost-effective and thus attractive solution to localization and mapping on smart ground vehicles. However, the accuracy and especially robustness of vision-only solutions remain rivalled by more expensive, lidar-based multi-sensor alternatives. We show that a significant increase in robustness can be achieved if taking non-holonomic kinematic constraints on the vehicle motion into account. Rather than using approximate planar motion models or simple, pair-wise regularization terms, we demonstrate the use of B-splines for an exact imposition of smooth, nonholonomic trajectories inside the 6 DoF bundle adjustment. We introduce both hard and soft formulations and compare their computational efficiency and accuracy against traditional solutions. Through results on both simulated and real data, we demonstrate a significant improvement in robustness and accuracy in degrading visual conditions.