Real‐time localization and elevation mapping within urban search and rescue scenarios

Abstract

Urban search and rescue ͑USAR͒ is a time critical task. One goal in rescue robotics is to have a team of heterogeneous robots that explore autonomously the disaster area, while jointly creating a map of the terrain and registering victim locations, which can further be utilized by human task forces for rescue. Basically, the robots have to solve autonomously in real-time the problem of simultaneous localization and mapping ͑SLAM͒, consisting of a continuous state estimation problem and a discrete data association problem. In this paper we contribute a novel method for efficient loop closure in harsh large-scale environments that utilizes RFID technology for data association and slippage-sensitive odometry for 2D pose tracking. Furthermore, we introduce a computational efficient method for building elevation maps by utilizing an extended Kalman filter for 3D pose tracking, which can be applied in real-time while navigating on rough terrain. The proposed methods have been extensively evaluated within outdoor environments, as well as within USAR test arenas designed by the National Institute of Standards and Technology ͑NIST͒. Our results show that the proposed methods perform robustly and efficiently within the utilized benchmark scenarios.

Mapping the Surroundings as a Requirement for Autonomous Driving

“…First the rapid mapping of a large-scale environment by wheeled robots, and second the mapping of rough terrain by tracked robots [7].…”

Hector Open Source Modules for Autonomous Mapping and Navigation with Rescue Robots

“…The elevation map is represented by a 2D grid map storing a height value with a corresponding variance for each cell. The cell measurement update is based on a local Kalman Filter and adapted from the approach described in [8].…”

Behavior maps for online planning of obstacle negotiation and climbing on rough terrain

“…Behavior maps are generated from elevation maps, which are two-dimensional arrays storing for each discrete location the corresponding height value and variance. We utilize a method for real-time elevation mapping on rough terrain that facilitates mapping while the robot navigates on uneven surfaces [12]. The method generates an elevation map by the successive integration of the robot's three-dimensional pose and range measurements of a downwards tilted Laser Range Finder (LRF).…”

“…Behavior maps are directly generated from elevation maps, i.e. two-dimensional grids storing in each cell the corresponding height of the terrain surface [3], [12], and a set of skill descriptions. Skill descriptions contain a set of fuzzy rules for the classification of structures they can handle, a set of spatial constraints encoding preconditions required for their execution, a 1 This research was partially supported by DFG as part of the collaborative research center SFB/TR-8 Spatial Cognition R7 cost function utilized for A* planning on the map, and the skill routine to be executed.…”