SLAMICP Library: Accelerating Obstacle Detection in Mobile Robot Navigation via Outlier Monitoring following ICP Localization

Abstract

The Iterative Closest Point (ICP) is a matching technique used to determine the transformation matrix that best minimizes the distance between two point clouds. Although mostly used for 2D and 3D surface reconstruction, this technique is also widely used for mobile robot self-localization by means of matching partial information provided by an onboard LIDAR scanner with a known map of the facility. Once the estimated position of the robot is obtained, the scans gathered by the LIDAR can be analyzed to locate possible obstacles obstructing the planned trajectory of the mobile robot. This work proposes to speed up the obstacle detection process by directly monitoring outliers (discrepant points between the LIDAR scans and the full map) spotted after ICP matching instead of spending time performing an isolated task to re-analyze the LIDAR scans to detect those discrepancies. In this work, a computationally optimized ICP implementation has been adapted to return the list of outliers along with other matching metrics, computed in an optimal way by taking advantage of the parameters already calculated in order to perform the ICP matching. The evaluation of this adapted ICP implementation in a real mobile robot application has shown that the time required to perform self-localization and obstacle detection has been reduced by 36.7% when obstacle detection is performed simultaneously with the ICP matching instead of implementing a redundant procedure for obstacle detection. The adapted ICP implementation is provided in the SLAMICP library.

A Retrospective Analysis of Indoor CO2 Measurements Obtained with a Mobile Robot during the COVID-19 Pandemic

“…In this work, the navigable areas are defined by a grid with nodes measuring 0.120 m × 0.120 m each, which is created using the Breadth-First Search (BFS) algorithm [59]. The final 2D point cloud map is created by joining the 2D scans obtained during the exploration by using a variant of the Iterative Closest Point (ICP) algorithm [54,57]. The result of this matching process is the determination of a common reference coordinate system for all the scans, allowing them to be unified in a single 2D map.…”

“…The method employed for robot self-localization relies on matching the current 2D scan provided by its 2D LIDAR sensor with the 2D map of the building floor by using the ICP algorithm [54,57]. This matching provides an estimate of the relative location and orientation of the robot on the 2D map.…”

“…This matching provides an estimate of the relative location and orientation of the robot on the 2D map. The obstacle detection procedure implemented in the robot is performed after the ICP matching [54,66]. The points of the scan retrieved by the LIDAR sensor which have not been matched with other points of the map are automatically identified as obstacles to be avoided.…”

“…This LIDAR has a radial distance range of up to 40 m, with a precision between 30 and 50 mm, covers a plane of 270 • around the sensor, and provides 1081 points per scan, at a rate of 40 scans/s. The mobile robot processes the information gathered by this 2D LIDAR for simultaneous localization and mapping (SLAM) [53], obstacle avoidance [54,55], and autonomous path-planning and path-tracking.…”

Analytical Formalism for Data Representation and Object Detection with 2D LiDAR: Application in Mobile Robotics

“…In cases where a map is already available, the use of 2D LiDAR is also attractive. For instance, a fast obstacle detection technique for mobile robot navigation using a 2D-LiDAR scan and a 2D map is proposed in [30].…”