Map-building and map-based localization in an underground-mine by statistical pattern matching
21
Citation
4
Reference
10
Related Paper
Citation Trend
Abstract:
This paper reports on the map-building and map-based localization of a load-haul-dump (LHD) truck in an underground mine using statistical pattern-matching techniques utilizing range images obtained from a scanning laser range-finder The map-building approach is based on an extended Kalman filter (EKF) and the resulting map is composed of poly-lines. Three approaches are proposed for the localization of the vehicle, namely the iterative closest point (ICP) approach, a reflective beacon based approach and the combined ICP-EKF approach, wherein, the last two approaches explicitly take into account the uncertainty associated with the observation data. These approaches are then compared using data gathered from an underground mine in Queensland, Australia for their relative merits subject to various factors and the corresponding results are presented.Keywords:
Iterative closest point
Map matching
Map matching
Mode (computer interface)
Road map
Cite
Citations (12)
Accurate and reliable localization is necessary for vehicle autonomous driving. Existing localization systems based on the GNSS cannot always provide lane-level accuracy. This paper proposes the method that improves vehicle localization by using road lanes recognized from a camera and a digital map. Iterative Closest Point (ICP) matching is performed for generated point clouds to minimize lateral error. The neural network is used for lane detection, detections are post-processed and fitted to the polynomial. Changes that allowed improving ICP matching are described. Finally, we perform an experiment with GPS RTK signal as ground truth and demonstrate that the proposed method has a position error of less than 0.5 m for vehicle localization.
Iterative closest point
Position (finance)
Map matching
Ground truth
Cite
Citations (1)
Because of the low estimation accuracy of normal extended Kalman filter( EKF) in strong nonlinear system,this paper developed an improved extended Kalman filter( MI-EKF) to solve the problem,and it improved the filtering accuracy greatly. It proposed MI-EKF by combining multi-innovation theory and the standard EKF. MI-EKF had better precision and stability,because MI-EKF considered not only the current measured value,but also gave full consideration to the time before state of motion. Finally,it discussed the impact of algorithm precision which included different numbers of innovations. Simulation results show that the improved algorithm MI-EKF included two innovations is optimal.
Cite
Citations (0)
This paper faces the state estimation problem for a nonlinear system, using an Extended Kalman Filter (EKF) that receives measurements taken by a set of wireless sensors.
Cite
Citations (2)
The problem of on-line calibration of dynamic traffic assignment (DTA) models is receiving increasing attention from researchers and practitioners. The problem can be formulated as a non-linear state-space model. Because of its nonlinear nature, the resulting model cannot be solved by the Kalman filter and therefore non-linear extensions need to be considered. In this paper, three extensions to the Kalman filter algorithm are presented: extended Kalman filter (EKF), limiting EKF (LimEKF), and unscented Kalman filter (UKF). The solution algorithms are applied to the calibration of the state-of-the-art DynaMIT-R DTA model and their use is demonstrated in a freeway network in Southampton, U.K. The LimEKF shows accuracy comparable to that of the best algorithm, but vastly superior computational performance
Alpha beta filter
Unscented transform
State-space representation
Line (geometry)
Cite
Citations (27)
This paper proposes a map-matching method for automotive navigation systems. The proposed method utilizes a Kalman filter algorithm and can achieve more accurate positioning than the correlation method. The performance of the algorithm is verified by simulation.
Map matching
Cite
Citations (40)
본 논문은 비선형 필터 기법에 따른 지형참조항법 성능 분석에 관한 연구를 수행하였다. 지형참조항법에 사용되는 기본 필터에는 확장 칼만 필터(EKF)가 있다. 본 연구는 EKF 원형외에 반복형 EKF(IEKF), stochastic linearization(SL) 조건이 추가된 EKF-SL과 unscented Kalman Filter(UKF) 알고리듬을 소개한다. 또한, 연속적(sequential) 필터 외에 일괄적(batch)필터 기법인 칼만 필터 무리(bank of Kalman filters)를 이용한 항법 기술도 비교군으로 추가하고 필터 간 항법 성능을 분석한다. 가상 궤적을 가진 항공기 시뮬레이션을 통해 초기위치 오차가 클 때도 강건한(robust) 필터로 stochastic linearization EKF가 선정되었으며, 다만 빠른 항법 해의 수렴이 요구될 때에는 칼만 필터 무리를 이용한 일괄적 필터가 효과적인 것으로 분석되었다. This paper focuses on a performance analysis of TRN among various nonlinear filtering methods. In a TRN research, extended Kalman filter(EKF) is a basic estimation algorithm. In this paper, iterated EKF(IEKF), EKF with stochastic linearization(SL), and unscented Kalman filter(UKF) algorithms are introduced to compare navigation performance with original EKF. In addition to introduced sequential filters, bank of Kalman filters method, which is one of the batch method, is also presented. Finally, by simulating an artificial aircraft mission, EKF with SL was chosen as the most consistent filter in the introduced sequential filters. Also, results suggested that the bank of Kalman filters can be alternative for TRN, when a fast convergence of navigation solution is needed.
Linearization
Alpha beta filter
Unscented transform
Nonlinear filter
Cite
Citations (2)
We describe a method for incorporating map information to the Kalman filter that is commonly used in indoor and outdoor navigation systems. The map information is provided as a measurement to the Kalman filter to ensure the consistency of the Kalman estimate. The proposed method provides huge computational saving over common map matching algorithms that use the more computationally expensive particle filter. We show indoor navigation examples that highlight the efficiency of the proposed algorithm.
Map matching
Alpha beta filter
Cite
Citations (3)
異なる視点から計測された画像を位置あわせて完全なモデル生成することはリバース・エンジニアリングに関する一つ研究である.本論文では特徴部分抽出に基づく効率的な位置あわせアルゴリズムを提示された.初期位置あわせで,特徴点を抽出され,特徴点の間の位置関係により特徴線を連結する.その後,Iterative Closest Point (ICP)によって精密位置あわせを行う.本研究の手法は普通のICPより,収束結果を上で,版蒙が早く,少ない重ねた部分のデータに対してり精度が高いことが判明することができる.
Iterative closest point
Cite
Citations (0)
In the present research, a nonlinear Kalman filtering approach, i.e., extended Kalman filter (EKF) was proposed to solve dynamic OD flows and travel times on a freeway segment. The non-linearity results from the facts that the coefficient matrices in the measurement equation of the Kalman filtering framework are unknown in advance and needed to be obtained/updated in light of the most recent observations. The numerical results demonstrated the capability of the proposed EKF model in the dynamic estimation of freeway OD demands and travel times. More significantly, one can design beneficial traffic control and management strategies in accordance with the estimation results.
Dynamic equation
Cite
Citations (5)