Andrey Soloviev

Tight coupling of GPS, laser scanner, and inertial measurements for navigation in urban environments

Many applications can be envisioned for accurate, robust, and reliable navigation solution in challenging urban environments. Examples of existing and prospective applications include, but are not limited to, navigation, guidance, and control of autonomous vehicles (including both ground and aerial vehicles) for urban surveillance and reconnaissance; collection of geographical information system (GIS) data in cities; monitoring of urban infrastructure for situational awareness; and, precise automotive applications such as automated lane keeping. If used by themselves, none of the existing navigation technologies have the potential to fully satisfy the requirements for reliable and accurate navigation in urban environments. Hence, this paper develops a multi-sensor integrated solution that combines the complementary features of the Global Positioning System (GPS), laser scanner feature-based navigation, and inertial navigation for urban scenarios. GPS and laser scanner-based navigation ideally complement each other for urban navigation. The laser scanner-based navigation relies on the availability of structures (lines and surfaces) within the scan range (80 m, typically). Features (such as lines) are first extracted from laser scan images and then used for position and attitude determination. In urban areas, if there exists a building wall that blocks GPS signals, this wall creates a feature in the laser scan image. On the other hand, for open streets with limited features, the GPS signal is generally unobstructed. Thus, GPS and laser data can be combined into integrated solution architecture. The system architecture developed also exploits INS navigation states for improved solution robustness: e.g., for robust feature association between scan images and for coasting through instances where sufficient number of combined GPS/laser measurements is unavailable. A tightly coupled GPS/laser scanner/INS mechanization is developed and applied for centimeter- accurate trajectory reconstruction. The paper uses live urban data to demonstrate that combined GPS and laser scanner data generally support the observability of navigation states at any part of the urban trajectory; and, for those limited cases where insufficient GPS/laser measurements are available, the INS coasting option can be efficiently utilized. Test results presented also show that the developed tightly coupled GPS/laser scanner/INS solution provides accurate trajectory reconstruction capabilities (one sigma error residuals are at a cm-level) in challenging urban environments.

Decoding Navigation Data Messages from Weak GPS Signals

A new method for decoding navigation data bits from very weak Global Positioning System (GPS) signals is presented. Coherent signal integration is applied over a time period that includes multiple navigation data bits. A search is performed for a combination of bits that maximizes the signal energy during the coherent integration.

Utilizing multipath reflections in deeply integrated GPS/INS architecture for navigation in urban environments

This paper investigates the constructive use of multipath reflections of Global Positioning System (GPS) signals for navigation in urban environments. Urban navigation applications are generally characterized by a significant presence of multipath signals. In order to maintain reliable and accurate navigation capabilities, it is critical to distinguish between direct signal and multipath. At the same time, multipath reflections can be exploited as additional measurements for those cases where the number of direct path satellites is insufficient to compute the navigation solution. The paper develops a method for the identification of multipath reflections in received satellite signals: i.e. multipath is separated from direct signal and a line-of-site between the GPS receiver and a multipath reflecting object is determined. Once multipath reflections are identified, they can be used constructively for navigation. The method presented in the paper exploits an open loop batch-processing GPS receiver, laser scanner and inertial navigation system (INS) to identify multipath reflections in received satellite signals. Experimental GPS, inertial and laser scanner data collected in real urban environments are applied to demonstrate identification of multipath reflections.

Implementation of a Flash-LADAR aided inertial navigator

GPS aided inertial systems can provide high accuracy position and attitude information in many operational scenarios; however, their use is limited to environments where GPS signals are available. An alternative method can be based on the use of Flash-LADAR or other 3D imaging sensor data to navigate in the GPS-devoid environments or the integration of these 3D imaging sensors with an inertial measurement unit. Flash LADAR (laser radar) and 3D imaging technology consists of a laser or light transmission source that is being emitted over a relatively large aperture coupled with a focal plane array detector capable of creating an ldquoimagerdquo of the observed scene where each pixel not only has an associated intensity value but also an estimate of the range to the observed object. With the recent development of short-range low-cost (Lt

Precise Velocity Estimation Using a Stand-Alone GPS Receiver

10 k) 3D imager technology, the use of such technology to aid a low-cost inertial measurement unit (IMU) has become practical and of great interest. Current low-cost 3D imager or Flash LADAR technology is capable of greater than 100 times 100 pixel resolution with 5 mm depth resolution at a 30 Hz frame rate. From this 3D range, features such as planar surfaces created by the walls, lines created where planes intersect, and points from the intersection of lines can be measured with high precision. In this paper we will focus the discussion on the 3D imager feature extraction performance aspects and lay the basis for the image and IMU-based feature association and integration architecture. Data from an indoor test will be utilized to illustrate some of the concepts explained in this paper..