Carita, Manuel (University of Alberta), Contreras Cabrera, Marcelo Jafett (University of Alberta), Hashemi, Ehsan (University of Alberta)

Robust 3D Bounding Box Detection and State Estimation of Dynamic Objects for Autonomous Navigation

Scheduled for presentation during the Regular Session "S43b-Multi-Sensor Fusion and Perception for Robust Autonomous Driving" (FR-EA-T43), Friday, November 21, 2025, 14:50−15:30, Stradbroke

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 18, 2025

Abstract

Accurate state estimation of dynamic objects is critical for safe and reliable autonomous navigation in dynamic urban settings. Real-world scenarios may include occlusions, drifting in pose estimation, data sparsity, or abrupt appearances, representing complexity in multi-object tracking. This paper addresses the challenges of estimating the states of non-ego vehicles during autonomous navigation using multimodal visual-LiDAR perception, without any need to global navigation systems. The proposed estimation framework integrates detection and tracking through an optimal variance filter. The first stage incorporates instance segmentation and point cloud association, followed by an L-shape fitting method to estimate 3D Bounding Boxes. Object tracking is then performed using a modified multi-object tracking algorithm augmented by the initialization of motion models. This enables estimation of consistent surrounding vehicle velocities and positions while keeping track of them in the 3D space, enhancing situational awareness for motion planning, and supporting collision avoidance using predictive models. Comprehensive experiments on the KITTI dataset demonstrate the effectiveness and accuracy of the proposed framework compared to current benchmarks and state-of-the-art methods.