RIPNEON: Memory-Lite and Computation-Efficient Occupancy Mapping via Block Read-Write and Key Grids Expansion

[Code] [bilibili]

Qianli Dong, Xuebo Zhang, Shiyong Zhang, Haobo Xi, Ziyu Wang, Zhe Ma, Haobo Xi, Zhiyong Zhang

IEEE Conference on Robotics and Automation (ICRA)Accepted. 2026

In this work, we propose a memory-lite and computation-efficient occupancy mapping algorithm for LiDAR-based robotic exploration plan- ning. To accelerate the query operation and reduce the memory usage, we adopt the grid-block as the basic data structure and propose to dynamically read and write blocks around the sensor. For each block, the occupied grids and frontier grids are maintained in two separate lists, serving as key grids for the map update. Instead of updating free grids by ray-racasting, we propose a key grids expansion algorithm to avoid repetitively querying grids on casted beams. The proposed algorithm not only speeds up the occupancy map update but also detects the frontier grids, which are crucial for exploration tasks, without extra computation. We compare the proposed method with state-of-the-art mapping methods on the KITTI dataset and a self-collected dataset.

Fast Exploration Planning with Learning-Based Motion Time Prediction for Aerial Robots

Ziyu Wang*, Qianli Dong*, Xuebo Zhang, Shiyong Zhang, Haobo Xi, Zhe Ma, Mingxing Yuan (* equal contribution)

IEEE Conference on Robotics and Automation (ICRA)Accepted. 2026

In this article, we propose an efficient dual-layer exploration planning method. The insight of our dual-layer planning method is efficiently finding an acceptable long-term region routing and greedily exploring the target in the first region of routing at high speed. Specifically, we propose a long-term region routing approximate algorithm, called “exploration-oriented heuristic double-tree algorithm”, to ensure real-time planning in large-scale environments. Then, the viewpoint in the first routing region with the highest curvature-penalized score, which can effectively reduce decelerations caused by sharp turn motions, will be chosen as the next exploration target. To further speed up the exploration, we propose an aggressive and safe exploration-oriented trajectory planning approach to enhance exploration continuity and speed. The proposed method is compared with state-of-the-art methods in challenging simulation environments.

EDEN: Efficient Dual-Layer Exploration Planning for Fast UAV Autonomous Exploration in Large 3-D Environments

[Code] [ieee] [arxiv] [bilibili] [youtube]

Qianli Dong, Xuebo Zhang, Shiyong Zhang, Ziyu Wang, Zhe Ma, Haobo Xi

IEEE Transactions on Industrial Electronics (T-IE) 2026

In this article, we propose an efficient dual-layer exploration planning method. The insight of our dual-layer planning method is efficiently finding an acceptable long-term region routing and greedily exploring the target in the first region of routing at high speed. Specifically, we propose a long-term region routing approximate algorithm, called “exploration-oriented heuristic double-tree algorithm”, to ensure real-time planning in large-scale environments. Then, the viewpoint in the first routing region with the highest curvature-penalized score, which can effectively reduce decelerations caused by sharp turn motions, will be chosen as the next exploration target. To further speed up the exploration, we propose an aggressive and safe exploration-oriented trajectory planning approach to enhance exploration continuity and speed. The proposed method is compared with state-of-the-art methods in challenging simulation environments.

FSMP: A Frontier-Sampling-Mixed Planner for Fast Autonomous Exploration of Complex and Large 3-D Environments

[ieee] [arxiv]

Shiyong Zhang, Xuebo Zhang, Qianli Dong, Ziyu Wang, Haobo Xi, Jing Yuan

IEEE Transactions on Instrumentation and Measurement (T-IM) 2025

In this article, we propose a systematic framework for fast exploration of complex and large 3-D environments using micro aerial vehicles (MAVs). The key insight is the organic integration of the frontier- and sampling-based strategies that can achieve rapid global exploration of the environment. Specifically, a field-of-view (FOV)-based frontier detector with the guarantee of completeness and soundness is devised for identifying 3-D map frontiers. Different from random sampling-based methods, the deterministic sampling technique is employed to build and maintain an incremental road map based on the recorded sensor FOVs and newly detected frontiers. With the resulting road map, we propose a two-stage path planner. First, it quickly computes the global optimal exploration path on the road map using the lazy evaluation strategy. Then, the best exploration path is smoothed to further improve the exploration efficiency.

HIGHSTAR: High-Speed and Efficient Online Autonomous UAV Exploration

[Code] [ieee]

Qianli Dong, Xuebo Zhang, Shiyong Zhang, Ziyu Wang, Zhe Ma, Tianyi Li, Haobo Xi

IEEE Transactions on Automation Science and Engineering (T-ASE) 2025

This paper presents a consistent, high-speed, and efficient online autonomous UAV exploration method. First, a motion primitive activated graph search method is proposed to fully take advantage of the UAV’s current velocity and acceleration. It improves motion time cost evaluation by simulating short-term motion tendencies with motion primitives and reduces the computational cost by searching on a voxel graph with a dynamic upper bound. Then, a minimum time trajectory to the optimal viewpoint with a non-zero terminal velocity constraint in a convex hull is optimized. Finally, an SE(3) coverage trajectory for unknown space around the exploration path is further optimized.