UMR CNRS 7253

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en:video [2014/03/12 10:41]
bonnif
en:video [2020/05/31 17:17] (current)
bonnif
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-====== This page contains several videos ======+====== Look at my YouTube channel ======
  
-===== Drivable space characterization [[http://www.youtube.com/embed/d4Bsrbnawxw|See the video]] =====+[[https://www.youtube.com/channel/UC1DKjKoeB70PwAfoqPcxxcw/videos|Click here]]
  
-The characterization in real-time of the drivable space in front of the vehicle is a key issue for safe autonomous navigation or driving assistance. This video presents a method that uses a lidar (a multilayer laser scanner) integrated in the front bumper of an automotive vehicle. A grid processing is first applied to detect and localize objects in the immediate environment after having compensated the movement of the vehicle. Accurate map information is then introduce in the perception scheme to refine the characterization of the drivable space. +===== Former videos =====
  
-===== Use of map data in GNSS computation fix =====+== Drivable space characterization  ==
  
-<html> +The characterization in real-time of the drivable space in front of the vehicle is a key issue for safe autonomous navigation or driving assistanceThis video presents a method that uses a lidar (a multilayer laser scanner) integrated in the front bumper of an automotive vehicle. A grid processing is first applied to detect and localize objects in the immediate environment after having compensated the movement of the vehicleAccurate map information is then introduce in the perception scheme to refine the characterization of the drivable space. [[http://www.youtube.com/embed/d4Bsrbnawxw|See the video]]
-<object width="425" height="350"> <param name="movie" value="http://www.youtube.com/v/ubjhBwpy8pA"> </param> <embed src="http://www.youtube.com/v/ubjhBwpy8pA" type="application/x-shockwave-flash" width="425" height="350"> </embed> </object> +
-</html>+
  
 +== Use of map data in GNSS computation fix ==
 In this video, we present a data fusion strategy relying principally on DR under the hypothesis that the DR performances are very good. When the localizer is tracking the pose, the map and the GPS observations are considered as doubtful. They are involved in the localisation process when they are consistent with the DR pose. This is carried out using integrity Chi-Square checks on innovations signals. When the vehicle approaches an ambiguity zone (detected thanks to the connectivity information of the map), the map information is not taken into account. If the map outage is too long, GPS data is also forgotten since it is not possible to detect signal propagation troubles. When leaving the ambiguity zone, the complete tracking starts again and checks the integrity of all the available information. In practice, we have developed a tightly-coupled Kalman filter which switches between the different observations modes. In this video, we present a data fusion strategy relying principally on DR under the hypothesis that the DR performances are very good. When the localizer is tracking the pose, the map and the GPS observations are considered as doubtful. They are involved in the localisation process when they are consistent with the DR pose. This is carried out using integrity Chi-Square checks on innovations signals. When the vehicle approaches an ambiguity zone (detected thanks to the connectivity information of the map), the map information is not taken into account. If the map outage is too long, GPS data is also forgotten since it is not possible to detect signal propagation troubles. When leaving the ambiguity zone, the complete tracking starts again and checks the integrity of all the available information. In practice, we have developed a tightly-coupled Kalman filter which switches between the different observations modes.
 +[[http://www.youtube.com/v/ubjhBwpy8pA|See the video]]
  
-===== Visual memory management ===== +== Visual memory management ==
-<html> +
-<object width="425" height="344"><param name="movie" value="http://www.youtube.com/v/55gbgNaZQlk&hl=en&fs=1"></param><param name="allowFullScreen" value="true"></param><embed src="http://www.youtube.com/v/55gbgNaZQlk&hl=en&fs=1" type="application/x-shockwave-flash" allowfullscreen="true" width="425" height="344"></embed></object> +
-</html> +
 This video Shows the problem of localizing a vehicle in urban environment by using natural information provided by exteroceptive sensors. For this purpose, sensors need to detect landmarks which have been characterized in a previous passage. As the amount of data can be significantly large, a strategy has been used to manage this information in a GIS (Geographical Information System). The developments have been illustrated using visual landmarks made of key images and 3D points that correspond to the roads of a GIS layer thanks to the use of GPS data and proprioceptive sensors. Real experiments are reported to illustrate the performance of this approach which is robust to GPS outages due to poor satellite visibility in urban areas. This video Shows the problem of localizing a vehicle in urban environment by using natural information provided by exteroceptive sensors. For this purpose, sensors need to detect landmarks which have been characterized in a previous passage. As the amount of data can be significantly large, a strategy has been used to manage this information in a GIS (Geographical Information System). The developments have been illustrated using visual landmarks made of key images and 3D points that correspond to the roads of a GIS layer thanks to the use of GPS data and proprioceptive sensors. Real experiments are reported to illustrate the performance of this approach which is robust to GPS outages due to poor satellite visibility in urban areas.
 +[[http://www.youtube.com/v/55gbgNaZQlk&hl=en&fs=1|See the video]]
 +

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