PIPO-SLAM: Lightweight Visual-Inertial SLAM With Preintegration Merging Theory and Pose-Only Descriptions of Multiple View Geometry

Optimization-based VI-SLAM focuses on the establishment of the loss function using both inertial and visual constraints. Preintegration theory is commonly used to express inertial constraints, but it lacks the merging equation between keyframes, challenging VI-SLAM from culling and merging redundant keyframes. To address this, we establish an on-manifold preintegration merging theory, including the merging of preintegrated terms, noise covariance, and Jacobians for bias updating, which significantly improves the preintegration theory and provides theoretical support for the keyframe management function of VI-SLAM. Visual constraints are typically expressed using multiple view geometry with three-dimensional (3D) points optimized as scene structure parameters. However, the excessive dimensionality of the optimization parameters generated by 3D points can lead to computational bottlenecks. Through the recent pose-only imaging geometry representation, we construct a lightweight optimization algorithm for SLAM that avoids the dimensional explosion in bundle adjustment (BA). Based on the above, we propose a 3D points-free SLAM optimizer. The proposed algorithms are validated on simulation, public datasets, and real-world experiments, and compared against advanced open-source systems such as ORB-SLAM3 and VINS.