IMMNet: Hybrid Fusion of Model-based and Data-driven Approaches for Maneuvering Target Tracking

Maneuvering target tracking in three-dimensional space remains a challenging problem due to complex motion dynamics and model mismatch. To address this, this paper proposes a hybrid model/data-driven algorithm named IMMNet, which integrates the interpretable structure of the interacting multiple model (IMM) algorithm with learnable neural components. Unlike end-to-end black-box methods, the proposed IMMNet algorithm not only can preserve the Bayesian inference mechanism that is essential for real-time radar applications, but also can adaptively learn motion patterns and noise characteristics from data. Extensive experiments demonstrate that the proposed IMMNet algorithm consistently outperforms the existing algorithms across various scenarios, validating it as a robust, interpretable, and practical solution for maneuvering target tracking.

Semantic Exploration and Dense Mapping of Complex Environments using Ground Robots Equipped with LiDAR and Panoramic Camera

This paper presents a system for autonomous semantic exploration and dense semantic target mapping of a complex unknown environment using a ground robot equipped with a LiDAR-panoramic camera suite. Existing approaches often struggle to balance collecting high-quality observations from multiple view angles and avoiding unnecessary repetitive traversal. To fill this gap, we propose a complete system combining mapping and planning. We first redefine the task as completing both geometric coverage and semantic viewpoint observation. We then manage semantic and geometric viewpoints separately and propose a novel Priority-driven Decoupled Local Sampler to generate local viewpoint sets. This enables explicit multi-view semantic inspection and voxel coverage without unnecessary repetition. Building on this, we develop a hierarchical planner to ensure efficient global coverage. In addition, we propose a Safe Aggressive Exploration State Machine, which allows aggressive exploration behavior while ensuring the robot's safety. Our system includes a plug-and-play semantic target mapping module that integrates seamlessly with state-of-the-art SLAM algorithms for pointcloud-level dense semantic target mapping. We validate our approach through extensive experiments in both realistic simulations and complex real-world environments. Simulation results show that our planner achieves faster exploration and shorter travel distances while guaranteeing a specified number of multi-view inspections. Real-world experiments further confirm the system's effectiveness in achieving accurate dense semantic object mapping of unstructured environments.