Multi-sensor three-dimensional Monte Carlo localization for long-term aerial robot navigation

Abstract

This article presents an enhanced version of the Monte Carlo localization algorithm, commonly used for robot navigation in indoor environments, which is suitable for aerial robots moving in a three-dimentional environment and makes use of a combination of measurements from an Red,Green,Blue-Depth (RGB-D) sensor, distances to several radio-tags placed in the environment, and an inertial measurement unit. The approach is demonstrated with an unmanned aerial vehicle flying for 10 min indoors and validated with a very precise motion tracking system. The approach has been implemented using the robot operating system framework and works smoothly on a regular i7 computer, leaving plenty of computational capacity for other navigation tasks such as motion planning or control.

Multi-Sensor Fusion for Aerial Robots in Industrial GNSS-Denied Environments

“…As previously stated, our algorithm is based on a Monte Carlo localization algorithm that was previously developed by the authors [25], which was based on an RGB-D camera as the main sensor for obtaining 3D point clouds of the environment. Taking into account the redesign of the aerial robot and the new tentative applications for industrial scenarios, there are several differences.…”

“…It can be noted that the errors are smaller by the end of the flight, especially in the Z axis. Therefore, it is worth comparing it with the algorithm proposed in [25]. For this reason, both Table 5 with the RMS values and the corresponding Figure 11 with the flight performed are attached.…”

“…The aforementioned approaches are promising in the sense that they can all provide solutions to the localization problem, but there are major drawbacks if they are used separately. The presented work builds on previous research from the authors [25], a multi-modal sensor fusion approach which is based on an enhanced MCL algorithm that integrates an odometry source for short-term estimations with other sources of 3D measurements, namely 3D point clouds of the environment, a previously built 3D map, and radio-based sensors installed in the environment and localized within such a 3D map. The main contribution of the presented work focuses on a similar combination of technologies, but with a stronger focus on achieving higher accuracy and robustness for the safe introduction of aerial robots in industrial applications, thanks to the use of a 3D LiDAR sensor.…”

DLL: Direct LIDAR Localization. A map-based localization approach for aerial robots

“…A pose tracking method based on 3D Monte Carlo localization and using the raw point clouds as observations is presented in [18]. 3D Distance Fields (DF) are used to represent the map.…”

“…The indoor dataset is composed by two fights released together with the AMCL3D ROS 2 module [18] for aerial robot localization indoors 3 . The scenario is shown in Fig.…”