Outdoor exploration and SLAM using a compressed filter
نویسندگان
چکیده
In this paper we describe the use of automatic exploration for autonomous mapping of outdoor scenes. We describe a real-time SLAM implementation along with an autonomous exploration algorithm. We have implemented SLAM with a compressed extended Kalman filter (CEKF) on an outdoor robot. Our implementation uses walls of buildings as features. The state predictions are made by using a combination of odometry and inertial data. The system was tested on a 200 x 200 m site with 18 buildings on variable terrain. The paper helps explain some of the implementation details of the compressed filter such as, how to organize the map as well as more general issues like, how to include the effects of pitch and roll and efficient feature detection.
منابع مشابه
Robust Simultaneous Localization and Mapping for Very Large Outdoor Environments
This paper addresses the problem of Simultaneous Localization and Mapping (SLAM) when working in very large environments. A Hybrid architecture is presented that makes use of the Extended Kalman Filter to perform SLAM in a very efficient form and a Monte Carlo type filter to resolve the data association problem potentially present when returning to a known location after a large exploration tas...
متن کاملResults for outdoor-SLAM using sparse extended information filters
In [13], a new algorithm was proposed for efficiently solving the simultaneous localization and mapping (SLAM) problem. In this paper, we extend this algorithm to handle data association problems and report real-world results, obtained with an outdoor vehicle. We find that our approach performs favorably when compared to the extended Kalman filter solution from which it is derived. in Proceedin...
متن کاملPii: S0921-8890(02)00233-6
9 This work presents real time implementation algorithms of simultaneous localization and map building (SLAM) with emphasis to outdoor land vehicle applications in large environments. It presents the problematic of outdoors navigation in areas with combination of feature and featureless regions. The aspect of feature detection and validation is investigated to reliably detect the predominant fe...
متن کاملGamma-SLAM: Stereo Visual SLAM in Unstructured Environments Using Variance Grid Maps
We introduce a new method for stereo visual SLAM (simultaneous localization and mapping) that works in unstructured, outdoor environments. Observations use dense stereo vision to measure the variance of the heights in each cell of a 2D grid. Unlike other grid-based SLAM algorithms, which use occupancy grid maps, our algorithm uses a new mapping technique that maintains a posterior distribution ...
متن کاملHybrid architecture for simultaneous localization and map building in large outdoor areas
This paper address the problem of navigating in very large outdoor unstructured environments. It presents solutions to the problem of closing large loops in simultaneous localization and map building applications. A hybrid architecture is presented that make use of the Extended Kalman Filter to perform SLAM in an efficient form and a Monte Carlo type filter to resolve the data association probl...
متن کامل