The collaboration of robots and humans sharing workspace, can increase productivity and reduce production costs. However, occupational accidents resulting in injuries can increase, by removing the physical safety around the robot, and allowing the human to enter the workspace of the robot. In preventing occupational accidents, studies on recognizing humans, by installing various sensors around the robot and responding to humans, have been proposed. Using the LiDAR (Light Detection and Ranging) sensor, a wider range can be measured simultaneously, which has advantages in that the LiDAR sensor is less impacted by the brightness of light, and so on. This paper proposes a simple and fast method to recognize humans, and estimate the path of humans using a single stationary 360° LiDAR sensor. The moving object is extracted from background using the occupied grid map method, from the data measured by the sensor. From the extracted data, a human recognition model is created using CNN machine learning method, and the hyper-parameters of the model are set, using a grid search method to increase accuracy. The path of recognized human is estimated and tracked by the extended Kalman filter.

This paper relates to the implementation of moving object position estimation by Pulsed LiDAR that can detect objects with high precision, speed, and spatial resolution. LiDAR measures the distance by calculating a return travel time when target is reflected. The retro-reflector, regardless of incident angle, can be reflected horizontally in the incident direction. This algorithm proposes a new approach method using LiDAR and retro-reflectors. According to the above algorithm, position can be determined by automatically detecting 90% of the reflected return beam intensity from moving objects to which the retro-reflector is attached. When this algorithm was applied indoors, it was possible to locate the position of the scanner accurately within ±5 mm error in 2,500 × 2,500 (mm) space. Also, it can detect a space of up to 5,000 × 5,000 (mm), making this an effective method for determining the position of a moving object in indoors.

Light detection and ranging (LiDAR) is one of the most efficient technologies to obtain the topographic and bathymetric map of coastal zones, superior to other technologies, such as sound navigation and ranging (SONAR) and synthetic aperture radar (SAR). However, the measurement results using LiDAR are vulnerable to environmental factors. To achieve a correspondence between the acquired LiDAR data and reality, error sources must be considered, such as the water surface slope, water turbidity, and seafloor slope. Based on the knowledge of those factors’ effects, error corrections can be applied. We concentrated on the effect of the seafloor slope on LiDAR waveforms while restricting other error sources. A simulation regarding in-water beam scattering was conducted, followed by an investigation of the correlation between the seafloor slope and peak timing of return waveforms. As a result, an equation was derived to correct the depth error caused by the seafloor slope.