DynaQuadric: Dynamic Quadric SLAM for Quadric Initialization, Mapping, and Tracking

Abstract

Dynamic SLAM is a key technology for autonomous driving and robotics, and accurate pose estimation of surrounding objects is important for semantic perception tasks. Current quadric SLAM methods are based on the assumption of a static environment and can only reconstruct static quadrics in the scene, which limits their applications in complex dynamic scenarios. In this paper, we propose a visual SLAM system that is capable of reconstructing dynamic objects as quadrics, with a unified framework for jointly optimizing pose estimation, multi-object tracking (MOT), and quadric parameters. We propose a robust object-centric quadric initialization algorithm for both static and moving objects, which decouples the prior estimation of the object pose from the quadric parameters. The object is initialized with a coarse sphere, and quadric parameters are further refined. We design a novel factor graph that tightly optimizes camera pose, object pose, map points and quadric parameters within the sliding window-based optimization. To the best of our knowledge, we are the first to propose a dynamic SLAM that combines quadric representations and MOT in a tightly coupled optimization. We perform qualitative and quantitative experiments on both simulated and real-world datasets, and demonstrate the robustness and accuracy in terms of camera localization, dynamic quadric initialization, mapping and tracking. Our system demonstrates the potential application of object perception with quadric representation in complex dynamic scenes.