Automated Ground Truth Estimation For Automotive Radar Tracking Applications With Portable GNSS And IMU Devices

Baseline generation for tracking applications is a difficult task when working with real world radar data. Data sparsity usually only allows an indirect way of estimating the original tracks as most objects' centers are not represented in the data. This article proposes an automated way of acquiring reference trajectories by using a highly accurate hand-held global navigation satellite system (GNSS). An embedded inertial measurement unit (IMU) is used for estimating orientation and motion behavior. This article contains two major contributions. A method for associating radar data to vulnerable road user (VRU) tracks is described. It is evaluated how accurate the system performs under different GNSS reception conditions and how carrying a reference system alters radar measurements. Second, the system is used to track pedestrians and cyclists over many measurement cycles in order to generate object centered occupancy grid maps. The reference system allows to much more precisely generate real world radar data distributions of VRUs than compared to conventional methods. Hereby, an important step towards radar-based VRU tracking is accomplished.Comment: 10 pages, 9 figures, accepted paper for 2019 20th International Radar Symposium (IRS), Ulm, Germany, June 2019. arXiv admin note: text overlap with arXiv:1905.1121

K-Means Fingerprint Clustering for Low-Complexity Floor Estimation in Indoor Mobile Localization

Indoor localization in multi-floor buildings is an important research problem. Finding the correct floor, in a fast and efficient manner, in a shopping mall or an unknown university building can save the users' search time and can enable a myriad of Location Based Services in the future. One of the most widely spread techniques for floor estimation in multi-floor buildings is the fingerprinting-based localization using Received Signal Strength (RSS) measurements coming from indoor networks, such as WLAN and BLE. The clear advantage of RSS-based floor estimation is its ease of implementation on a multitude of mobile devices at the Application Programming Interface (API) level, because RSS values are directly accessible through API interface. However, the downside of a fingerprinting approach, especially for large-scale floor estimation and positioning solutions, is their need to store and transmit a huge amount of fingerprinting data. The problem becomes more severe when the localization is intended to be done on mobile devices which have limited memory, power, and computational resources. An alternative floor estimation method, which has lower complexity and is faster than the fingerprinting is the Weighted Centroid Localization (WCL) method. The trade-off is however paid in terms of a lower accuracy than the one obtained with traditional fingerprinting with Nearest Neighbour (NN) estimates. In this paper a novel K-means-based method for floor estimation via fingerprint clustering of WiFi and various other positioning sensor outputs is introduced. Our method achieves a floor estimation accuracy close to the one with NN fingerprinting, while significantly improves the complexity and the speed of the floor detection algorithm. The decrease in the database size is achieved through storing and transmitting only the cluster heads (CH's) and their corresponding floor labels.Comment: Accepted to IEEE Globecom 2015, Workshop on Localization and Tracking: Indoors, Outdoors and Emerging Network

Integrated Inertial/gps

The presence of failures in navigation sensors can cause the determination of an erroneous aircraft state estimate, which includes position, attitude, and their derivatives. Aircraft flight control systems rely on sensor inputs to determine the aircraft state. In the case of integrated Inertial/NAVSTAR Global Positioning System (GPS), sensor failures could occur in the on-board inertial sensors or in the GPS measurements. The synergistic use of both GPS and the Inertial Navigation System (INS) allows for highly reliable fault detection and isolation of sensor failures. Integrated Inertial/GPS is a promising technology for the High Speed Civil Transport (HSCT) and the return and landing of a manned space vehicle

Fault detection and isolation

Erroneous measurements in multisensor navigation systems must be detected and isolated. A recursive estimator can find fast growing errors; a least squares batch estimator can find slow growing errors. This process is called fault detection. A protection radius can be calculated as a function of time for a given location. This protection radius can be used to guarantee the integrity of the navigation data. Fault isolation can be accomplished using either a snapshot method or by examining the history of the fault detection statistics

Pedestrian detection in uncontrolled environments using stereo and biometric information

A method for pedestrian detection from challenging real world outdoor scenes is presented in this paper. This technique is able to extract multiple pedestrians, of varying orientations and appearances, from a scene even when faced with large and multiple occlusions. The technique is also robust to changing background lighting conditions and effects, such as shadows. The technique applies an enhanced method from which reliable disparity information can be obtained even from untextured homogeneous areas within a scene. This is used in conjunction with ground plane estimation and biometric information,to obtain reliable pedestrian regions. These regions are robust to erroneous areas of disparity data and also to severe pedestrian occlusion, which often occurs in unconstrained scenarios

Sensor node localisation using a stereo camera rig

In this paper, we use stereo vision processing techniques to detect and localise sensors used for monitoring simulated environmental events within an experimental sensor network testbed. Our sensor nodes communicate to the camera through patterns emitted by light emitting diodes (LEDs). Ultimately, we envisage the use of very low-cost, low-power, compact microcontroller-based sensing nodes that employ LED communication rather than power hungry RF to transmit data that is gathered via existing CCTV infrastructure. To facilitate our research, we have constructed a controlled environment where nodes and cameras can be deployed and potentially hazardous chemical or physical plumes can be introduced to simulate environmental pollution events in a controlled manner. In this paper we show how 3D spatial localisation of sensors becomes a straightforward task when a stereo camera rig is used rather than a more usual 2D CCTV camera