Real‐Time SLAM with Octree Evidence Grids for Exploration in Underwater Tunnels

TLDR

The paper proposes a SLAM method for a hovering underwater vehicle to explore 3‑D underwater caves and tunnels. The method uses a Rao‑Blackwellized particle filter with a 3‑D evidence grid map, dynamically adjusts particle count for real‑time performance, adapts the prediction step for sensor degradation, and employs an efficient octree data structure to maintain hundreds of maps for large environments. The octree structure reduces processing and storage by exploiting spatial locality and shared ancestry, and the method was validated on real‑world datasets from Wakulla Springs, Sistema Zacatón, and the DEPTHX test tank. © 2007 Wiley Periodicals, Inc.

Abstract

Abstract We describe a simultaneous localization and mapping (SLAM) method for a hovering underwater vehicle that will explore underwater caves and tunnels, a true three‐dimensional (3D) environment. Our method consists of a Rao‐Blackwellized particle filter with a 3D evidence grid map representation. We describe a procedure for dynamically adjusting the number of particles to provide real‐time performance. We also describe how we adjust the particle filter prediction step to accommodate sensor degradation or failure. We present an efficient octree data structure that makes it feasible to maintain the hundreds of maps needed by the particle filter to accurately model large environments. This octree structure can exploit spatial locality and temporal shared ancestry between particles to reduce the processing and storage requirements. To test our SLAM method, we utilize data collected with manually deployed sonar mapping vehicles in the Wakulla Springs cave system in Florida and the Sistema Zacato´n in Mexico, as well as data collected by the DEPTHX vehicle in the test tank at the Austin Applied Research Laboratory. We demonstrate our mapping and localization approach with these real‐world datasets. © 2007 Wiley Periodicals, Inc.