On campus, the complexity of the environment and the lack of regulatory constraints make it difficult to model the environment, resulting in less efficient motion planning algorithms. To solve this problem, HD-Map-guided sampling-based motion planning is a feasible research direction. We proposed a motion planning algorithm for autonomous vehicles on campus, called HD-Map-guided rapidly-exploring random tree (HDM-RRT). In our algorithm, A collision risk map (CR-Map) that quantifies the collision risk coefficient on the road is combined with the Gaussian distribution for sampling to improve the efficiency of algorithm. Then, the node optimization strategy of the algorithm is deeply optimized through the prior information of the CR-Map to improve the convergence rate and solve the problem of poor stability in campus environments. Three experiments were designed to verify the efficiency and stability of our approach. The results show that the sampling efficiency of our algorithm is four times higher than that of the Gaussian distribution method. The average convergence rate of the proposed algorithm outperforms the RRT* algorithm and DT-RRT* algorithm. In terms of algorithm efficiency, the average computation time of the proposed algorithm is only 15.98 ms, which is much better than that of the three compared algorithms.

Removing moving objects from 3D LiDAR data plays a crucial role in advancing real-time odometry, life-long SLAM, and motion planning for robust autonomous navigation. In this paper, we present a novel method aimed at addressing the challenges faced by existing approaches when dealing with scenarios involving significant registration errors. The proposed approach offers a unique solution for removing moving objects without the need for registration, leveraging range flow estimation combined with IMU measurements. To this end, our method performs global range flow estimation by utilizing geometric constraints based on the spatio-temporal gradient information derived from the range image, and we introduce IMU measurements to further enhance the accuracy of range flow estimation. Through extensive quantitative evaluations, our approach showcases an improved performance, with an average mIoU of 45.8%, surpassing baseline methods such as Removert (43.2%) and Peopleremover (32.2%). Specifically, it exhibits a substantial improvement in scenarios characterized by a deterioration in registration performance. Moreover, our method does not rely on costly annotations, which make it suitable for SLAM systems with different sensor setups.

Trajectory planning in tight spaces presents a significant challenge due to the complex maneuvering required under kinematic and obstacle avoidance constraints. When obstacles are densely distributed near the target state, the limited connectivity between the feasible states and terminal state can further decrease the efficiency and success rate of trajectory planning. To address this challenge, we propose a novel Dual-Stage Motion Pattern Tree (DS-MPT) algorithm. DS-MPT decomposes the trajectory generation process into two stages: merging and posture adjustment. Each stage utilizes specific heuristic information to guide the construction of the trajectory tree. Our experimental results demonstrate the high robustness and computational efficiency of the proposed method in various parallel parking scenarios. Additionally, we introduce an enhanced driving corridor generation strategy for trajectory optimization, reducing computation time by 54% to 84% compared to traditional methods. Further experiments validate the improved stability and success rate of our approach.

Autonomous parking trajectory planning in unstructured environments with irregular obstacles must address the challenges posed by non-convex obstacle constraints. Due to the coupling between problem complexity and the number of surrounding obstacles, existing methods often suffer from increased computation time and failure rates in dense environments. To tackle this issue, this paper proposes an efficient parking trajectory planner with fast obstacle retrieval capability. The proposed planner follows a classical two-stage architecture consisting of front-end trajectory generation and back-end trajectory optimization. In the first stage, a multi-resolution sampling-based trajectory generation method is introduced. By using multi-resolution sampling strategy and a KD-Tree-based collision detection algorithm, the method achieves high-quality initial trajectory while maintaining computational efficiency. In the second stage, we present a novel iterative trajectory optimization method based on neighbor obstacle indexing, which restricts the scale of obstacle constraints during each optimization iteration, which improves computational efficiency of trajectory optimization. Extensive comparative studies and real-car experiments are conducted to validate the advantages of the proposed method, particularly in terms of computation time and robustness in complex, cluttered environments.