Dinesh Manandhar

Vehicle-borne laser mapping system (VLMS) for 3-D GIS

We have developed a vehicle-borne laser mapping system (VLMS), which consists of Laser Scanners, Line Cameras, GPS and INS. The system uses range data, scanned by the laser scanners as the main source of 3D data. In this paper, we present about the development of VLMS, which includes the system architecture, calibration and geo-referencing of sensors and positioning devices. Next, we present the extraction of some basic features like building surfaces, road surfaces (man made features) and trees (natural features). Finally, we will show how these features can be used to build 3D urban GIS database, which will assist in various applications as mentioned above. Besides, we will also discuss about the capabilities and difficulties of such a system.

Accuracy assessment and improvement for level survey using real time kinematic (RTK) GPS

The Global Positioning System (GPS) is a satellite based positioning and navigation system that provides precise position, velocity and time information. The height accuracy in GPS is lower than the planimetric accuracy. This paper discusses about the height accuracy achievable in real time kinematic mode using single frequency GPS with Novatels RT20 algorithm. Analysis was also done to see whether there is any correlation between the antenna velocity (speed) and the accuracy. As a core part of the research, a procedural was developed to improve the height accuracy based on the observed data in RTK mode. This was accomplished by using two calibration points as reference and distributing the errors among the observed points. This improved the accuracy to a few centimeters level (2-4 cm, RMS), which is a very significant improvement.

Vehicle-borne Laser Mapping System (VLMS)-a new observation system for 3-D mapping of urban areas

We have developed a new observation system for mapping urban areas in three dimensions. This. system is called Vehicle-borne Laser Mapping System (VLMS). The system consists of three laser scanners and six line cameras for data acquisition. GPS, INS and Odometer are used for position data. Laser scanners are used as primary data source for building 3-D spatial data. Line camera images are used only for texture. In this paper, we present system architecture, accuracy analysis of the system and feature extraction from range data. Features are basically categorized as man-made features (buildings, roads, utility facilities, tunnels etc), natural features (trees, plants etc) and dynamic/static features (parked vehicle, moving vehicles, pedestrians).

Authentication technology using QZSS

We address in this paper about authentication of QZSS L1C/A signal using QZSS L1SAIF signal by developing an anti-spoofing methodology. The methodology is based on transmitting a signature data embedded into L1SAIF navigation message. The signature data is generated by using a part of L1C/A navigation message to generate Reference Authentication NAV Data (RAND). The RAND data is further encoded by LDPC based on a H-matrix. The LDPC encoded data is called signature data and is formatted to make it compatible with L1SAIF navigation message structure. This data is broadcasted from QZSS L1SAIF with a new message ID for authentication purpose. The receiver receives this message and decodes the authentication message into RAND and LDPC parity bits. Based on the information of RAND, the receiver gets corresponding H-matrix and other data from Authentication Data Center (ADC). These data from ADC is used to perform LDPC encoding to received RAND data. If the parity bits from this encoding are the same as the parity bits received by the receiver from L1SAIF signal are the same, it is concluded that the signals (L1C/A and L1SAIF) are authentic. Since, this method is based on using L1C/A navigation message for RAND and L1SAIF for broadcasting the signature data, it can also be implemented to other satellite systems like GPS L1C/A, MSAS, EGNOS and GAGAN.