Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-3/W4, 27-34, 2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
17 Mar 2016
A. M. Manzino and C. Taglioretti DIATI Department, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy
Keywords: Mobile Mapping, odometry, motion models, UKF, filtering techniques, sensor integration Abstract. The aim of this study is to identify the most powerful motion model and filtering technique to represent an urban terrestrial mobile mapping (TMM) survey and ultimately to obtain the best representation of the car trajectory. The authors want to test how far a motion model and a more or less refined filtering technique could bring benefits in the determination of the car trajectory.

To achieve the necessary data for the application of the motion models and the filtering techniques described in the article, the authors realized a TMM survey in the urban centre of Turin by equipping a vehicle with various instruments: a low-cost action-cam also able to record the GPS trace of the vehicle even in the presence of obstructions, an inertial measurement system and an odometer.

The results of analysis show in the article indicate that the Unscented Kalman Filter (UKF) technique provides good results in the determination of the vehicle trajectory, especially if the motion model considers more states (such as the positions, the tangential velocity, the angular velocity, the heading, the acceleration). The authors also compared the results obtained with a motion model characterized by four, five and six states.

A natural corollary to this work would be the introduction to the UKF of the photogrammetric information obtained by the same camera placed on board the vehicle. These data would permit to establish how photogrammetric measurements can improve the quality of TMM solutions, especially in the absence of GPS signals (like urban canyons).

Conference paper (PDF, 5160 KB)

Citation: Manzino, A. M. and Taglioretti, C.: ODOMETRY AND LOW-COST SENSOR FUSION IN TMM DATASET, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-3/W4, 27-34,, 2016.

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