Adaptive Mobile Robot Navigation and Mapping

Abstract

The task of building a map of an unknown environment and concurrently using that map to navigate is a central problem in mobile robotics research. This paper addresses the problem of how to perform concurrent mapping and localization (CML) adaptively using sonar. Stochastic mapping is a feature-based approach to CML that generalizes the extended Kalman filter to incorporate vehicle localization and environmental mapping. The authors describe an implementation of stochastic mapping that uses a delayed nearest neighbor data association strategy to initialize new features into the map, match measurements to map features, and delete out-of-date features. The authors introduce a metric for adaptive sensing that is defined in terms of Fisher information and represents the sum of the areas of the error ellipses of the vehicle and feature estimates in the map. Predicted sensor readings and expected dead-reckoning errors are used to estimate the metric for each potential action of the robot, and the action that yields the lowest cost (i.e., the maximum information) is selected. This technique is demonstrated via simulations, in-air sonar experiments, and underwater sonar experiments. Results are shown for (1) adaptive control of motion and (2) adaptive control of motion and scanning. The vehicle tends to explore selectively different objects in the environment. The performance of this adaptive algorithm is shown to be superior to straight-line motion and random motion. Nomenclature F dynamic model H observation model M transformation relating the Fisher information between time steps recursively

Adaptive Informative Path Planning in Metric Spaces

“…Although active localization [6] and simultaneous localization and mapping (SLAM) [5] bear some similarity to IPP, they are in fact different, because IPP assumes that the robot location is fully observable. Reducing active localization or SLAM to IPP incurs significant representational and computational cost.…”

Untitled

“…The classical target tracking literature provides a number of methods for data-association (Bar-Shalom & Fortmann, 1988;Popoli & Blackman, 1999) that are used in computer vision (Cox, 1993) and CML (Cox & Leonard, 1994;Feder, Leonard, & Smith, 1999), such as the track splitting filter (Zhang & Faugeras, 1992), the Joint Probabilistic Data Association Filter (JPDAF) (Rasmussen & Hager, 1998), and the multiple hypothesis tracker (MHT) (Reid, 1979;Cox & Leonard, 1994;Cox & Hingorani, 1994). Unfortunately the latter, more powerful methods have exponential complexity so suboptimal approximations are used in practice.…”

Autonomous Learning Architecture for Environmental Mapping

“…For instance, the frontier-based exploration of Yamauchi (1997), where the border lines between unknown and known regions, called frontiers, drives the robot behavior through a depth-first search. The Fisher information on the measurement and navigational uncertainties can also be used as heuristic functions (Feder et al, 1999). In this case the robot prefers actions that maximize its information gain in Fisher's sense.…”