Manipulation in cluttered environments is challenging due to spatial dependencies among objects, where an improper manipulation order can cause collisions or blocked access. Existing approaches often overlook these spatial relationships, limiting their flexibility and scalability. To address these limitations, we propose OrderMind, a unified spatial-aware manipulation ordering framework that directly learns object manipulation priorities based on spatial context. Our architecture integrates a spatial context encoder with a temporal priority structuring module. We construct a spatial graph using k-Nearest Neighbors to aggregate geometric information from the local layout and encode both object-object and object-manipulator interactions to support accurate manipulation ordering in real-time. To generate physically and semantically plausible supervision signals, we introduce a spatial prior labeling method that guides a vision-language model to produce reasonable manipulation orders for distillation. We evaluate OrderMind on our Manipulation Ordering Benchmark, comprising 163,222 samples of varying difficulty. Extensive experiments in both simulation and real-world environments demonstrate that our method significantly outperforms prior approaches in effectiveness and efficiency, enabling robust manipulation in cluttered scenes.

The integration of multiple LiDAR sensors has the potential to significantly enhance odometry systems by providing comprehensive environmental measurements. However, current multiple LiDAR-inertial odometry frameworks face challenges in real-time processing due to the voluminous data generated. This paper introduces a real-time, computationally efficient multiple LiDAR-inertial odometry system (Multi-LIO) that outperforms existing state-of-the-art solutions in accuracy and scalability. Utilizing a novel parallel strategy for state updates and a voxelized map format, Multi-LIO optimizes computational efficiency. Furthermore, we introduce a point-wise uncertainty estimation method to augment the accuracy of scan-to-map registration, particularly in large-scale and complex scenarios. We validate our system’s performance through extensive experiments on various challenging sequences. Multi-LIO emerges as a robust, scalable, and extensible solution, adaptable to various LiDAR configurations.

Traditional monocular visual simultaneous localization and mapping (SLAM) systems rely on point features or line features to estimate and optimize the camera trajectory and build a map of the surrounding environment. However, in complex scenarios such as underground parking, the performance of traditional point-line SLAM systems tends to degrade due to mirror reflection, illumination change, poor texture, and other interference. This paper proposes VPL-SLAM, a structural vertical line supported point-line monocular SLAM system that works well in complex environments such as underground parking or campus. The proposed system leverages structural vertical lines at all instances of the process. With the assistance of the structural vertical lines and global vertical direction, our system can output a more accurate visual odometry result. Furthermore, the resulting map of our system is a more reasonable structural line feature map than the previous point-line-based monocular SLAM systems. Our system has been tested with the popular autonomous driving dataset Kitti Odometry. In addition, to fully test the proposed SLAM system, we also test our system using a self-collected dataset, including underground parking and campus scenarios. As a result, our proposal reveals a more accurate navigation result and a more reasonable structural resulting map compared to state-of-the-art point-line SLAM systems such as Structure PLP-SLAM.