High-performance 3D mapping of unknown environments using parallel computing for mobile robots

Abstract

Autonomous multi-robot systems are a popular research field in the 3D mapping of unknown environments. High fault tolerance, increased accuracy, and low latency in coverage are the main reasons why a multi-robot system is preferred over a single robot in an unpredictable field. Compared with 3D scene reconstruction which is a conceptually similar but resource-wise different technique, autonomous mobile robot 3D mapping techniques are missing a crucial element. Since most mobile robots run on low computationally powered processing units, the real-time registration of point clouds into high-resolution 3D occupancy grid maps is a challenge. Until recently, it was nearly impossible to perform parallel point cloud registration in mobile platforms. Serial processing of a large amount of high-frequency input data leads to buffer overflows and failure to include all information into the 3D map. With the introduction of Graphical Processing Units (GPUs) into commodity hardware, mobile robot 3D mapping now can achieve faster time performance, using the same algorithmic techniques as 3D scene reconstruction. However, parallelization of mobile robot 3D occupancy grid mapping process is a less frequently discussed topic. As a Central Processing Unit (CPU) is necessary to run conventional middleware, operating system, and hardware drivers, the system is developed as a CPU-GPU mixed pipeline. The precomputed free scan mask is used to accelerate the process of identifying free voxels in space. Point positional information is transformed into unsinged integer coordinates to cope with Morton codes, which is a linear representation of octree nodes instead of traditional spatial octrees. 64-bit M-codes and 32-bit RGBO-codes are stored in a hash table to reduce access time compared to a hierarchical octree. Point cloud transformation, ray tracing, mapping point coordinated into integer scale, Morton-coded voxel generation, RGBO-code generation are the processes that are performed inside the GPU. Retrieving point cloud information, map update using bitwise operations and map publish are executed within the CPU. Additionally, a multi-robot system is prototyped as a team of wheeled robots autonomously exploring an unknown, even-surfaced environment, while building and merging fast 3D occupancy grid maps and communicating using a multi-master communication protocol.