Incremental Light Bundle Adjustment for Robotics Navigation
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This paper presents a new computationally-efficient method for vision-aided navigation (VAN) in autonomous robotic applications. While many VAN approaches are capable of processing incoming visual observations, incorporating loop-closure measurements typically requires performing a bundle adjustment (BA) optimization, that involves both all the past navigation states and the observed 3D points. Our approach extends the incremental light bundle adjustment (LBA) method, recently developed for structure from motion , to information fusion in robotics navigation and in particular for including loop-closure information. Since in many robotic applications the prime focus is on navigation rather then mapping, and as opposed to traditional BA, we algebraically eliminate the observed 3D points and do not explicitly estimate them. Computational complexity is further improved by applying incremental inference. To maintain high-rate performance over time, consecutive IMU measurements are summarized using a recently-developed technique and navigation states are added to the optimization only at camera rate. If required, the observed 3D points can be reconstructed at any time based on the optimized robot’s poses. The proposed method is compared to BA both in terms of accuracy and computational complexity in a statistical simulation study.