Self-localization Estimation Research Articles

Optimal Maneuvering for Autonomous Vehicle Self-Localization.

We consider the problem of optimal maneuvering, where an autonomous vehicle, an unmanned aerial vehicle (UAV) for example, must maneuver to maximize or minimize an objective function. We consider a vehicle navigating in a Global Navigation Satellite System (GNSS)-denied environment that self-localizes in two dimensions using angle-of-arrival (AOA) measurements from stationary beacons at known locations. The objective of the vehicle is to travel along the path that minimizes its position and heading estimation error. This article presents an informative path planning (IPP) algorithm that (i) uses the determinant of the self-localization estimation error covariance matrix of an unscented Kalman filter as the objective function; (ii) applies an l-step look-ahead (LSLA) algorithm to determine the optimal heading for a constant-speed vehicle. The novel algorithm takes into account the kinematic constraints of the vehicle and the AOA means of measurement. We evaluate the performance of the algorithm in five scenarios involving stationary and mobile beacons and we find the estimation error approaches the lower bound for the estimator. The simulations show the vehicle maneuvers to locations that allow for minimum estimation uncertainty, even when beacon placement is not conducive to accurate estimation.

Multi-sensor fusion for underwater robot self-localization using PC/BC-DIM neural network

PurposeSelf-localization of an underwater robot using global positioning sensor and other radio positioning systems is not possible, as an alternative onboard sensor-based self-location estimation provides another possible solution. However, the dynamic and unstructured nature of the sea environment and highly noise effected sensory information makes the underwater robot self-localization a challenging research topic. The state-of-art multi-sensor fusion algorithms are deficient in dealing of multi-sensor data, e.g. Kalman filter cannot deal with non-Gaussian noise, while parametric filter such as Monte Carlo localization has high computational cost. An optimal fusion policy with low computational cost is an important research question for underwater robot localization.Design/methodology/approachIn this paper, the authors proposed a novel predictive coding-biased competition/divisive input modulation (PC/BC-DIM) neural network-based multi-sensor fusion approach, which has the capability to fuse and approximate noisy sensory information in an optimal way.FindingsResults of low mean localization error (i.e. 1.2704 m) and computation cost (i.e. 2.2 ms) show that the proposed method performs better than existing previous techniques in such dynamic and unstructured environments.Originality/valueTo the best of the authors’ knowledge, this work provides a novel multisensory fusion approach to overcome the existing problems of non-Gaussian noise removal, higher self-localization estimation accuracy and reduced computational cost.

Analysis of Occlusion Effects for Map-Based Self-Localization in Urban Areas.

A high-definition (HD) map provides structural information for map-based self-localization, enabling stable estimation in real environments. In urban areas, there are many obstacles, such as buses, that occlude sensor observations, resulting in self-localization errors. However, most of the existing HD map-based self-localization evaluations do not consider sudden significant errors due to obstacles. Instead, they evaluate this in terms of average error over estimated trajectories in an environment with few occlusions. This study evaluated the effects of self-localization estimation on occlusion with synthetically generated obstacles in a real environment. Various patterns of synthetic occlusion enabled the analyses of the effects of self-localization error from various angles. Our experiments showed various characteristics that locations susceptible to obstacles have. For example, we found that occlusion in intersections tends to increase self-localization errors. In addition, we analyzed the geometrical structures of a surrounding environment in high-level error cases and low-level error cases with occlusions. As a result, we suggested the concept that the real environment should have to achieve robust self-localization under occlusion conditions.

Teammate Advanced Drive System Using Automated Driving Technology

<div class="section abstract"><div class="htmlview paragraph">Woven Core, Inc. has developed Teammate Advanced Drive, a driving support system with state-of-the-art automated driving technology based on the Mobility Teammate Concept by Toyota Motor Corporation. Teammate Advanced Drive enables intelligent Ramp to Ramp hands-off driving on highways. The system features a self-localization estimation system that uses an HD-Map (High Definition Map) and high-level redundancy across sensors, computing, actuators, power supplies, and data communication. The system also includes digital data uploading and downloading capabilities wirelessly OTA (Over the Air) in order to provide customers the latest map updates as well as new software features and upgraded performance. A number of characteristically unique sensors have been combined to monitor the entire perimeter of the vehicle with high reliability. The perception system combines sensing data from radars, newly developed LiDAR (Light Detection and Ranging), and cameras for redundant omnidirectional detection. Teammate Advanced Drive provides high-performance computing by combining multiple advanced SoC (System on Chip) and highly reliable MCU (Micro Controller Unit), in order to achieve real-time execution of multi-processing system components, including deep neural networks. In order to provide enhanced safety, a fail-safe operation system, which utilizes a high-redundancy structure, has been developed to takeover for the driver when a malfunction or a system limitation is detected. This includes an Emergency Driving Stop System, which is able to guide the vehicle to the road shoulder if the system detects that the human driver is incapacitated or otherwise unable to drive the vehicle manually.</div></div>

The hybrid Cram\xe9r-Rao lower bound for simultaneous self-localization and room geometry estimation

This paper addresses the problem of tracking a moving source, e.g., a robot, equipped with both receivers and a source, that is tracking its own location and simultaneously estimating the locations of multiple plane reflectors. We assume a noisy knowledge of the robot’s movement. We formulate this problem, which is also known as simultaneous localization and mapping (SLAM), as a hybrid estimation problem. We derive the extended Kalman filter (EKF) for both tracking the robot’s own location and estimating the room geometry. Since the EKF employs linearization at every step, we incorporate a regulated kinematic model, which facilitates a successful tracking. In addition, we consider the echo-labeling problem as solved and beyond the scope of this paper. We then develop the hybrid Cramér-Rao lower bound on the estimation accuracy of both the localization and mapping parameters. The algorithm is evaluated with respect to the bound via simulations, which shows that the EKF approaches the hybrid Cramér-Rao bound (CRB) (HCRB) as the number of observation increases. This result implies that for the examples tested in simulation, the HCRB is an asymptotically tight bound and that the EKF is an optimal estimator. Whether this property is true in general remains an open question.

Self-Localization Estimation for Mobile Robot Based on Map-Matching Using Downhill Simplex Method

This paper describes a map-matching method which utilizes a downhill simplex method for self-localization estimation of a mobile robot for indoor and outdoor application. Although particle filter is widely established as a method of map-matching, it requires considerable time for recovery when the correct position is unidentifiable. One of the features of the downhill simplex method proposed in this paper is that the search point distribution is wide when it is challenging to determine a point as the correct position. However, it immediately shrinks when the correct position is identified. In this study, it is compared with particle filter and demonstrates the effectiveness of the proposed method through a discussion on the difference between the search methods.

Indoor Self-Localization and Orientation Estimation of Smartphones Using Acoustic Signals

We propose a new acoustic self-localization and orientation estimation algorithm for smartphones networks composed of commercial off-the-shelf devices equipped with two microphones and a speaker. Each smartphone acts as an acoustic transceiver, which emits and receives acoustic signals. Node locations are found by combining estimates of the range and direction of arrival (DoA) between node pairs using a maximum likelihood (ML) estimator. A tailored optimization algorithm is proposed to simultaneously solve the DoA uncertainty problem that arises from the use of only 2 microphones per node and obtain the azimuthal orientation of each node without requiring an electronic compass.

2A2-Q02 多数のRFIDリーダとICタグテキスタイルを用いた移動ロボットのための自己位置同定(移動ロボットの自己位置推定と地図構築)

Accurate self-localization system is necessary for a mobile robot to work in a home environment. Studies of a indoor autonomous mobile robot often use vision sensors and laser range finders. It is difficult to estimate the exact global location if the environment has number of places that have similar shape boundaries or small number of landmarks to localize. Furthermore, it tends to take a long time to recover the self-localization estimation if it goes wrong at once. Self-localization system based on reading RFID-tags on floor is robust against changes of lighting condition, obstacles, furniture and doors conditions in the environment. This paper proposes a self-localization method based on multiple RFID readers and dense IC tag textile and shows its validity in a real environment.

Recognition of Indoor Environment by Robot Partner Using Conversation

To support daily life before performing an action, a robot partner must perceive an unknown environment. Much research has been done from various viewpoints on self-localization estimation and environment perception. In our research, the robot partner performs self-localization and environment recognition using Simultaneous Localization and Mapping for self-localization estimation and map building. In this paper, we propose a method for recognizing indoor environments by robot partners based on conversations with human beings. Information acquired from maps is identified in order to share the meaning with human beings after the required interpretation. In this paper, we therefore propose a method for recognizing environmental maps by labeling these maps based on symbolic information developed through conversation with human beings. The proposed method is composed of four parts. First, the robot partner applies a steady-state genetic algorithm for self-localization estimation. Second, we use a map building algorithm for expressing the topological map. Third, conversation with human beings is performed for acquiring symbolic information in order to recognize object and position locations through the map. Fourth, we perform experiments and discuss the effectiveness of the proposed technique.

211 水中における自己位置推定システムの開発(ロボティクスII、知能機械)

211 水中における自己位置推定システムの開発(ロボティクスII、知能機械)

How a mobile robot selects landmarks to make a decision based on an information criterion

Most current mobile robots are designed to determine their actions according to their positions. Before making a decision, they need to localize themselves. Thus, their observation strategies are mainly for self-localization. However, observation strategies should not only be for self-localization but also for decision making. We propose an observation strategy that enables a mobile robot to make a decision. It enables a robot equipped with a limited viewing angle camera to make decisions without self-localization. A robot can make a decision based on a decision tree and on prediction trees of observations constructed from its experiences. The trees are constructed based on an information criterion for the action decision, not for self-localization or state estimation. The experimental results with a four legged robot are shown and discussed.

Acquisition of world images and self‐localization estimation using viewing image sequences

AbstractWe propose a method for robotic self‐localization estimation that can process position information without identifying special landmarks. This method, which combines viewpoint shifts with visual information about the environment, makes it possible for a robot to move both autonomously and purposefully. In our procedures, the robot surveys the landscape of an environment from multiple directions, obtaining self‐localization from a viewing image sequence. The robot is made aware of changes in the landscape via self‐organizing maps (SOM), which generate “concept patterns”. By making the SOM hierarchical, these concept patterns can be consolidated. This allows the robot to move both autonomously and purposefully in the environment toward a position by using previously collected information. By performing a travel experiment with the robot in an indoor environment, in which characteristic concept patterns recording topologies had been previously generated at various positions during a learning period, we confirmed that a correct self‐localization estimate can be generated from landscape changes detected via viewpoint shifts. © 2002 Wiley Periodicals, Inc. Syst Comp Jpn, 34(1): 68–78, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/scj.1185