At 110m in diameter and over 350m in depth, the cenote Zacatón in central Mexico is a unique flooded sinkhole. A platform for conducting preliminary sonar tests is tethered in place. 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 which 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 Zacatón 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 realworld datasets.
Fairfield et al. REF use evidence grids inside a particle filter to perform real-time 3D mapping of a sinkhole with an imaging sonar.
Real-time slam with octree evidence grids for exploration in underwater tunnels
Journal of Field Robotics
Engineering Computer Science