Development of the Autonomous Mobile Robot for Target-Searching in Urban Areas in the Tsukuba Challenge 2013

Large position errors plague GNSS-based sensors (e.g., GPS) due to poor satellite configuration and multipath effects, resulting in frequent outliers. Due to quadratic cost functions when optimizing SLAM via nonlinear least square methods, a single such outlier can cause severe map distortions. Following in the footsteps of recent improvements in the robustness of SLAM optimization process, this work presents a framework for improving sensor noise characterizations by combining a machine learning approach with max-mixture error models. By using max-mixtures, the sensor's noise distribution can be modeled to a desired accuracy, with robustness to outliers. We apply the framework to the task of accurately modeling the uncertainties of consumer-grade GPS sensors. Our method estimates the observation covariances using only weighted feature vectors and a single max operator, learning parameters off-line for efficient on-line calculation.

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Robust sensor characterization via max-mixture models: GPS sensors

Nowadays, satellite-based localization is a well-established technical solution to support several navigation tasks in daily life. Besides the application inside of portable devices, satellite-based positioning is used for in-vehicle navigation systems as well. Moreover, due to its global coverage and the availability of inexpensive receiver hardware it is an appealing technology for numerous applications in the area of Intelligent Transportation Systems (ITSs). However, it has to be admitted that most of the aforementioned examples either rely on modest accuracy requirements or are not sensitive to temporary integrity violations. Although technical concepts of Advanced Driver Assistance Systems (ADASs) based on Global Navigation Satellite Systems (GNSSs) have been successfully demonstrated under open sky conditions, practice reveals that such systems suffer from degraded satellite signal quality when put into urban areas. Thus, the main research objective of this thesis is to provide a reliable vehicle positioning concept which can be used in urban areas without the aforementioned limitations. Therefore, an integrated probabilistic approach which preforms fault detection & exclusion, localization and multi-sensor data fusion within one unified Bayesian framework is proposed. From an algorithmic perspective, the presented concept is based on a probabilistic data association technique with explicit handling of outlier measurements as present in urban areas. By that approach, the accuracy, integrity and availability are improved at the same time, that is, a consistent positioning solution is provided. In addition, a comprehensive and in-depth analysis of typical errors in urban areas within the pseudorange domain is performed. Based on this analysis, probabilistic models are proposed and later on used to facilitate the positioning algorithm. Moreover, the presented concept clearly targets towards mass-market applications based on low-cost receivers and hence aims to replace costly sensors by smart algorithms. The benefits of these theoretical contributions are implemented and demonstrated on the example of a real-time vehicle positioning prototype as used inside of the European research project GAlileo Interactive driviNg (GAIN). This work describes all necessary parts of this system including GNSS signal processing, fault detection and multi-sensor data fusion within one processing chain. Finally, the performance and benefits of the proposed concept are examined and validated both with simulated and comprehensive real-world sensor data from numerous test drives.

Bayesian Approach for Reliable GNSS-based Vehicle Localization in Urban Areas

A robotic work tool system (200) comprising a charging station (210) and a robotic work tool (100), said robotic work tool (100) comprising a position determining device (190) for determining a current position, the robotic work tool (100) being configured to determine that reliable navigation through said position determining device (190) is no longer possible, such as when satellite signal reception is not possible, at a time point (T1) and position and in response thereto generate an obstacle map which gives information on the position of at least one obstacle (260), determine when an area will be shadowed with regards to satellite reception based on said obstacle map, and to schedule operation of the robotic work tool (100) accordingly.

Navigation for a robotic working tool

Development of Method Using a Combination of DGPS and Scan Matching for the Making of Occupancy Grid Maps for Localization

This paper proposes a localization method using Global Positioning System(GPS) with multi-path estimation which uses 3D environment maps. Multi-path is one of the problems upon using the GPS for localization. GPS satellite visibility is one of the solutions to detect multi-path error. The challenge of the paper is to use pre-measured 3D environment map to check the satellite visibility. In order to compute the satellite visibility using 3D maps, the GPS receiver position is necessary, which is contradicted requirements to localization. The proposed method solves the problem by using particle filter. In this paper, This paper describes the GPS measurement model with satellite visibility using 3D maps, speeding up the satellite visibility check using GPS shadow maps, and experimental results in outdoor environment with multipath data.

GPS measurement model with satellite visibility using 3D map for particle filter

GPS Based Particle Filter Localization Method with Multipath Model using 3D-Map

移動ロボットが屋外環境にて自律的に動作するためには,多 数のセンサを用い,多数の処理を実行する必要がある.また,安 定・安全な自律移動を実現するためには,屋外環境において生じ る様々な問題に対し,知見を蓄積し,共有していくことが重要 である.そのためには,移動ロボットの機能をモジュール化し, 加法的に開発ができるようにするとともに,モジュールを共有 し,再利用することで知見を蓄積できる仕組みが必要である. 本研究では,そのような移動ロボットのモジュール化を実現 するため,RT-Middleware [1]を用いて,自律移動ロボット用 のモジュール群の開発を行っている [2]. 移動ロボットの機能をモジュール化し,機能の追加変更を容 易にするためには,機能の切り分け方が重要となる.すなわち, それぞれのシステムにて分離する場所が異なる場合,接合部が 合わず交換できない場合が生じる.そのため,性能を落とさず, 柔軟に変更できる切り分け方を検討する必要がある. 自律移動ロボットの構成要素において,外界センサは特に環 境に依存しやすく,環境に応じて適したセンサへの変更が必要 となる.筆者らは,自律移動ロボットにおいて外界センサに依存 する機能,すなわち位置推定機能と障害物検知機能に対し,外 界センサの追加変更が容易に可能な枠組みを提案した [3].これ

Masashi Yamazaki

Papers

GPS measurement model with satellite visibility using 3D map for particle filter

GPS Based Particle Filter Localization Method with Multipath Model using 3D-Map

Localization in Outdoor Environments using Multi-Sensor Localization Framework