Drones have become essential in various applications, but conventional quadrotors face limitations in confined spaces and complex tasks. Deformable drones, which can adapt their shape in real-time, offer a promising solution to overcome these challenges, while also enhancing maneuverability and enabling novel tasks like object grasping. This paper presents a novel approach to autonomous motion planning and control for deformable quadrotors. We introduce a shape-adaptive trajectory planner that incorporates deformation dynamics into path generation, using a scalable kinodynamic A* search to handle deformation parameters in complex environments. The backend spatio-temporal optimization is capable of generating optimally smooth trajectories that incorporate shape deformation. Additionally, we propose an enhanced control strategy that compensates for external forces and torque disturbances, achieving a 37.3% reduction in trajectory tracking error compared to our previous work. Our approach is validated through simulations and real-world experiments, demonstrating its effectiveness in narrow-gap traversal and multi-modal deformable tasks.

In the process of urban environment mapping, the sequential accumulations of dynamic objects will leave a large number of traces in the map. These traces will usually have bad influences on the localization accuracy and navigation performance of the robot. Therefore, dynamic objects removal plays an important role for creating clean map. However, conventional dynamic objects removal methods usually run offline. That is, the map is reprocessed after it is constructed, which undoubtedly increases additional time costs. To tackle the problem, this paper proposes a novel method for online dynamic objects removal for ground vehicles. According to the observation time difference between the object and the ground where it is located, dynamic objects are classified into two types: suddenly appear and suddenly disappear. For these two kinds of dynamic objects, we propose downward retrieval and upward retrieval methods to eliminate them respectively. We validate our method on SemanticKITTI dataset and author-collected dataset with highly dynamic objects. Compared with other state-of-the-art methods, our method is more efficient and robust, and reduces the running time per frame by more than 60% on average. Our method will be open-sourced on GitHub1.

In complex indoor structural scenes such as shopping centers and malls, camera pose estimation using pure point features is easy to fail due to the difficulty in extracting sufficient and stable point features from weak textures or dynamic environments. Recent works have attempted to address these challenges by introducing line features. However, the addition of line features increases the number of parameters and landmarks for BA (Bundle Adjustment), leading to efficiency reduction. This is a common issue in multi-modal SLAM (Simultaneous Localization And Mapping). To address this issue, this paper proposes a novel visual-depth-inertial odometry (ASML-VDIO) framework by combining RGB-D and IMU sensors. To improve the efficiency of BA, the proposed landmark classification method classifies 3D landmarks into accurate landmarks and other landmarks based on spatial consistency verification and depth range limitation. Then, accurate landmarks are fixed, and only other landmarks are optimized in the optimization of BA. Furthermore, to remove line features extracted from dynamic objects (pedestrian, shopping-car, etc), we propose a dynamic line removal method that combines geometric constraints and motion constraints of line features. Finally, the method is evaluated on public and author-collected datasets, showing competitive accuracy and robustness in complex indoor structural scenes while 71% speedup on optimization thread with same constraints.

Multi-sensor fusion systems have been widely applied in various fields, including mobile robot, simultaneous localization and mapping (SLAM), and autonomous driving. For a tightly coupled multi-sensor fusion system, strict time synchronization between sensors will improve the accuracy of the system. However, there is currently a lack of open-source and general-purpose hardware synchronization systems for Cameras, IMUs, LiDARs, GNSS/RTK in the academic community. Therefore, we propose EverySync, an open hardware time synchronization system to address this gap. The synchronization accuracy of the system was evaluated through multiple experiments, achieving an accuracy of less than 1 ms. And, real-world experiments proved that hardware time synchronization improves the accuracy of the SLAM system. This open-source system is available on GitHub.

Aerial-ground vehicles have shown great potential in various fields due to their superior mobility and outstanding endurance. However, most of morphing aerial-ground vehicles consider little about controllability and traversability in ground mode. We present a novel aerial-ground vehicle called TransRotor. By proposing a differential gear joint, we equip TransRotor with omnidirectional mobility in both air and ground mode. Besides, using a four-wheel-steering model in ground mode provides better traversability and ground flexibility. Moreover, we design mid-mode transformation for Trans-Rotor, which provides smooth and rapid mode switching. In this work, we firstly propose a novel design of an aerial-ground vehicle. Then, we propose a decoupled controller considering the four-wheel-steer model to achieve autonomous navigation of the vehicle. Comprehensive experiments and a benchmark comparison are carried out to validate the outstanding performance of the proposed system, where the system shows ground flexibility and saves energy up to more than 95%.

Three-dimensional point cloud map plays an important role in 3-D reconstruction, autonomous robot navigation, autonomous driving, and environmental monitoring. Nowadays, 3-D point cloud map could be obtained through frame-by-frame accumulation of LiDAR point cloud using SLAM technology. However, during this process, the movements of dynamic objects in the environment will leave a large number of traces on the point cloud map, causing difficulties in the subsequent use of the map, such as city model construction and robot autonomous navigation. Therefore, dynamic traces removal is crucial for building clean static maps. However, existing methods for dynamic traces removal are mostly offline, which inevitably incurs additional time consumption. To address this problem, this article proposes an online dynamic traces removal method. We take voxels as the smallest unit for dynamic traces removal, and voxels containing dynamic traces are called dynamic voxels, otherwise they are called static voxels. Our method is based on the assumption that static voxels always appear and disappear simultaneously with the ground below them. Therefore, we call voxel that appears later than the ground as suddenly appear dynamic voxel, and voxel that disappears earlier than the ground as suddenly disappear dynamic voxel. We call this method of judging dynamic voxels as observation time difference, and propose downward retrieval and upward retrieval methods to remove these two types of dynamic voxels, respectively. We tested our proposed method on SemanticKITTI, UrbanLoco, and author-collected datasets. Experimental results show that our method is more accurate and robust than existing online dynamic traces removal methods. And compared with other methods, our method shortens the time of processing each frame of point cloud by more than 60%. Our method is open-sourced on GitHub:https://github.com/RongguangWu/OTD.