Consistent map building in petrochemical complexes for firefighter robots using SLAM based on GPS and LIDAR

Abstract

The objective of this study was to achieve simultaneous localization and mapping (SLAM) of firefighter robots for petrochemical complexes. Consistency of the SLAM map is important because human operators compare the map with aerial images and identify target positions on the map. The global positioning system (GPS) enables increased consistency. Therefore, this paper describes two Rao-Blackwellized particle filters (RBPFs) based on GPS and light detection and ranging (LIDAR) as SLAM solutions. Fast-SLAM 1.0 and Fast-SLAM 2.0 were used in grid maps for RBPFs in this study. We herein propose the use of Fast-SLAM to combine GPS and LIDAR. The difference between the original Fast-SLAM and the proposed method is the use of the log-likelihood function of GPS; the proposed combination method is implemented using a probabilistic mathematics formulation. The proposed methods were evaluated using sensor data measured in a real petrochemical complex in Japan ranging in size from 550-380 m. RTK-GPS data was used for the GPS measurement and had an availability of 56%. Our results showed that Fast-SLAM 2.0 based on GPS and LIDAR in a dense grid map produced the best results. There was significant improvement in alignment to aerial data, and the mean square root error was 0.65 m. To evaluate the mapping consistency, accurate 3D point cloud data measured by Faro Focus 3D (± 3 mm) was used as the ground truth. Building sizes were compared; the minimum mean errors were 0.17 and 0.08 m for the oil refinery and management building area and the area of a sparse building layout with large oil tanks, respectively. Consequently, a consistent map, which was also consistent with an aerial map (from Google Maps), was built by Fast-SLAM 1.0 and 2.0 based on GPS and LIDAR. Our method reproduced map consistency results for ten runs with a variance of ± 0.3 m. Our method reproduced map consistency results with a global accuracy of 0.52 m in a low RTK-Fix-GPS environment, which was a factory with a building layout similar to petrochemical complexes with 20.9% of RTK-Fix-GPS data availability.

P3-LOAM: PPP/LiDAR Loosely Coupled SLAM With Accurate Covariance Estimation and Robust RAIM in Urban Canyon Environment

“…It is worth mentioning that Carrier-Phase Differential GPS was used in [38]. Shamsudin et al [42] extended Rao-Blackwellized particle filtering to fuse RTK and 3D LiDAR. He et al [39] used an optimization-based algorithm to integrate RTK and 3D LiDAR in partially GNSS-denied environment.…”

Integrated Indoor Positioning System of Greenhouse Robot Based on UWB/IMU/ODOM/LIDAR

“…The method is only dependent on a single ultrasonic sensor to locate and navigate the robot, which is susceptible to environmental interference and has poor flexibility. LIDAR, a novel type of navigation and positioning sensor, has become the mainstream sensor of robot navigation for its advantages of high stability, high precision, and high instantaneity [10,11]. Hou Jialin et al [12] used the integration of front and rear dual-lidar and wheel encoder based on Simultaneous Localization and Mapping (SLAM) algorithm to achieve the global positioning, mapping, and navigation functions of the robot in the greenhouse.…”

GNSS/IMU/ODO/LiDAR-SLAM Integrated Navigation System Using IMU/ODO Pre-Integration

“…Therefore, this fulfills the complementary advantages of multiple sensors. There are two main methods for multi-sensor data fusion: filtering [ 11 , 12 , 13 , 14 ] and graph optimization [ 15 , 16 , 17 ]. Compared with the former, the latter is more accurate and robust, but time-consuming [ 18 ].…”