By moving a commercial 2D LiDAR, 3D maps of the environment can be built, based on the data of a 2D LiDAR and its movements. Compared to a commercial 3D LiDAR, a moving 2D LiDAR is more economical. A series of problems need to be solved in order for a moving 2D LiDAR to perform better, among them, improving accuracy and real-time performance. In order to solve these problems, estimating the movements of a 2D LiDAR, and identifying and removing moving objects in the environment, are issues that should be studied. More specifically, calibrating the installation error between the 2D LiDAR and the moving unit, the movement estimation of the moving unit, and identifying moving objects at low scanning frequencies, are involved. As actual applications are mostly dynamic, and in these applications, a moving 2D LiDAR moves between multiple moving objects, we believe that, for a moving 2D LiDAR, how to accurately construct 3D maps in dynamic environments will be an important future research topic. Moreover, how to deal with moving objects in a dynamic environment via a moving 2D LiDAR has not been solved by previous research.

The state-of-the-art visual simultaneous localization and mapping (V-SLAM) systems have high accuracy localization capabilities and impressive mapping effects. However, most of these systems assume that the operating environment is static, thereby limiting their application in the real dynamic world. In this paper, by fusing the information of an RGB-D camera and two encoders that are mounted on a differential-drive robot, we aim to estimate the motion of the robot and construct a static background OctoMap in both dynamic and static environments. A tightly coupled feature-based method is proposed to fuse the two types of information based on the optimization. Dynamic pixels occupied by dynamic objects are detected and culled to cope with dynamic environments. The ability to identify the dynamic pixels on both predefined and undefined dynamic objects is available, which is attributed to the combination of the CPU-based object detection method and a multiview constraint-based approach. We first construct local sub-OctoMaps by using the keyframes and then fuse the sub-OctoMaps into a full OctoMap. This submap-based approach gives the OctoMap the ability to deform, and significantly reduces the map updating time and memory costs. We evaluated the proposed system in various dynamic and static scenes. The results show that our system possesses competitive pose accuracy and high robustness, as well as the ability to construct a clean static OctoMap in dynamic scenes.

Bionic propulsion has advantages over traditional blade propellers, such as efficiency and noise control. Existing research on ray-inspired robot fish has mainly focused on a single type of pectoral fin as bionic propeller, which only performed well in terms of pure speed or maneuverability. Rarely has the performance of different fin types been compared on the same platform to find an optimal solution. In this paper, a modularized robot fish with high-fidelity biomimetic pectoral fins and novel multi-DOF propelling mechanism is presented. A kinematic model of the pectoral fin based on motion analysis of a cownose ray is introduced as guidance for the propelling mechanism design. A high-fidelity parametric geo-model is established and evaluated based on statistical data. The design and fabrication process of the 3D soft bionic fins, as well as the robot platform, is also elaborated. Through experiments comparing the performance of different fin types constructed with different materials and approaches, it was found that the new soft fins made of silicon rubber have better performance than traditional fins constructed with a flexible inner skeleton and a permeable outer skin as a result of better 3D profile preservation and hydrodynamic force interaction. The robot ray prototype also acquires a better combination of high speed and maneuverability compared to results of previous research.

Fish can perceive the surrounding flow field using their lateral line systems, consisting of canal neuromasts (CNs) for flow pressure gradient perception and superficial neuromasts (SNs) for flow velocity detection. Although various artificial lateral line (ALL) systems have been developed inspired by the lateral line of fish, few studies have combined both pressure and velocity sensors for hydrodynamic perception, and how to optimize sensor placement remains unanswered, particularly on three-dimensional models. Herein we proposed a maximized hydrodynamic stimulation strategy for sensor placement optimization. We built a fish model and designed an ALL system by mimicking the geometry of a blind cavefish, Sinocyclocheilus tianlinensis . Differential pressure sensors and hot-film flow velocity sensors were used to mimic the CNs and SNs, respectively, with optimized sensor placement. The simulation and experimental results on yaw angle detection suggested that both sensor placement of our ALL system and distribution of the biological lateral line showed good agreement with the maximized hydrodynamic stimulation strategy. Our ALL system could detect a wall within a distance of 0.1 body length, comparable to the perception ability of cave fish. The new sensor placement strategy can be used to equip ALL systems on bionic robotic fish and other underwater vehicles.