Real-Time RGB-D Simultaneous Localization and Mapping Guided by Terrestrial LiDAR Point Cloud for Indoor 3-D Reconstruction and Camera Pose Estimation

A Forest Point Cloud Real-Time Reconstruction Method with Single-Line Lidar Based on Visual–IMU Fusion

In order to accurately obtain tree growth information from a forest at low cost, this paper proposes a forest point cloud real-time reconstruction method with a single-line lidar based on visual–IMU fusion. We build a collection device based on a monocular camera, inertial measurement unit (IMU), and single-line lidar. Firstly, pose information is obtained using the nonlinear optimization real-time location method. Then, lidar data are projected to the world coordinates and interpolated to form a dense spatial point cloud. Finally, an incremental iterative point cloud loopback detection algorithm based on visual key frames is utilized to optimize the global point cloud and further improve precision. Experiments are conducted in a real forest. Compared with a reconstruction based on the Kalman filter, the root mean square error of the point cloud map decreases by 4.65%, and the time of each frame is 903 μs; therefore, the proposed method can realize real-time scene reconstruction in large-scale forests.

Perspectives of RealSense and ZED Depth Sensors for Robotic Vision Applications

This review paper presents an overview of depth cameras. Our goal is to describe the features and capabilities of the introduced depth sensors in order to determine their possibilities in robotic applications, focusing on objects that might appear in applications with high accuracy requirements. A series of experiments was conducted, and various depth measuring conditions were examined in order to compare the measurement results of all the depth cameras. Based on the results, all the examined depth sensors were appropriate for applications where obstacle avoidance and robot spatial orientation were required in coexistence with image vision algorithms. In robotic vision applications where high accuracy and precision were obligatory, the ZED depth sensors achieved better measurement results.

Painting Path Planning for a Painting Robot with a RealSense Depth Sensor

The utilization of stereo cameras in robotic applications is presented in this paper. The use of a stereo depth sensor is a principal step in robotics applications, since it is the first step in sequences of robotic actions where the intent is to detect and extract windows and obstacles that are not meant to be painted from the surrounding wall. A RealSense D435 stereo camera was used for surface recording via a real-time, appearance-based (RTAB) mapping procedure, as well as to navigate the painting robot. Later, wall detection and the obstacle avoidance processes were performed using statistical filtering and a random sample consensus model (RANSAC) algorithm.

Development of a Low Cost and Path-free Autonomous Patrol System Based on Stereo Vision System and Checking Flags

Nowadays, security guard patrol services are becoming roboticized. However, high construction prices and complex systems make patrol robots difficult to be popularized. In this research, a simplified autonomous patrolling robot is proposed, which is fabricated by upgrading a wheeling household robot with stereo vision system (SVS), radio frequency identification (RFID) module, and laptop. The robot has four functions: independent patrolling without path planning, checking, intruder detection, and wireless backup. At first, depth information of the environment is analyzed through SVS to find a passable path for independent patrolling. Moreover, the checkpoints made with RFID tag and color pattern are placed in appropriate positions within a guard area. While a color pattern is detected by the SVS, the patrolling robot is guided to approach the pattern and check its RFID tag. For more, the human identification function of SVS is used to detect an intruder. While a skeleton information of the human is analyzed by SVS, the intruder detection function is triggered, then the robot follows the intruder and record the images of the intruder. The recorded images are transmitted to a server through Wi-Fi to realize the remote backup, and users can query the recorded images from the network. Finally, an experiment is made to test the functions of the autonomous patrolling robot successfully.