Abstract
This paper proposes a unique Graph SLAM framework to generate precise 2.5D LIDAR maps in an XYZ plane. A node strategy was invented to divide the road into a set of nodes. The LIDAR point clouds are smoothly accumulated in intensity and elevation images in each node. The optimization process is decomposed into applying Graph SLAM on nodes’ intensity images for eliminating the ghosting effects of the road surface in the XY plane. This step ensures true loop-closure events between nodes and precise common area estimations in the real world. Accordingly, another Graph SLAM framework was designed to bring the nodes’ elevation images into the same Z-level by making the altitudinal errors in the common areas as small as possible. A robust cost function is detailed to properly constitute the relationships between nodes and generate the map in the Absolute Coordinate System. The framework is tested against an accurate GNSS/INS-RTK system in a very challenging environment of high buildings, dense trees and longitudinal railway bridges. The experimental results verified the robustness, reliability and efficiency of the proposed framework to generate accurate 2.5D maps with eliminating the relative and global position errors in XY and Z planes. Therefore, the generated maps significantly contribute to increasing the safety of autonomous driving regardless of the road structures and environmental factors.
Highlights
Maps are a very important pillar to enable autonomous driving by encoding the real world with good weather factors and environmental conditions
The intensity and elevation maps are encoded by dividing the road into a set of nodes, The intensity and elevation maps are encoded by dividing the road into a set of and each node represents an environment area in the real world
As edge represents a loop-closure between nodes and in order accurate to highlight
Summary
Maps are a very important pillar to enable autonomous driving by encoding the real world with good weather factors and environmental conditions. Extracted features are are classified planner groups by Levenberg-Marquardti the Levenberg-Marquardti classified intointo edgeedge andand planner groups andand usedused by the opoptimization technique estimatethe thevehicle vehicleposes posesininconsecutive consecutivescans Another method timization technique toto estimate called LIO-SAM has been proposed to incorporate the estimated motion from an inertial measurement unit unit with with the results of LIDAR scan matching for optimizing the vehicle measurement trajectory [11]. 2.5Dmaps mapsare arevery verypromising promising components to power autonomous vehicles beThe components to power autonomous vehicles because cause of reducing the storing size, providing dense details, decreasing data represenof reducing the storing size, providing dense details, decreasing the datathe representation and tation and sufficiently enabling real-time processes compared to point cloud maps. Z (GS-Z), and the integration strategy in the XY plane (GS-XY) and elevation in the Z plane (GS-Z), and the integration strategyof both maps in the Absolute
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