A view-based outdoor navigation using object recognition robust to changes of weather and seasons

Katsura, Hitomi; Miura, Jun; Hild, Michael; Shirai, Yoshiaki

doi:10.1109/iros.2003.1249323

Cited by 44 publications

(22 citation statements)

References 10 publications

(2 reference statements)

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“…Yet, the interpretation of changing environmental conditions can pose serious challenges for computer vision processes, such as those associated with place recognition, navigation, road/terrain detection, and scene exploration. [19][20][21][22][23][24] This is because rain, snow, and fog weather events, smoke, haze, or other changes in lighting and visibility can significantly obscure features, degrade object recognition, and modify the saliency and image context of an outdoor scene. [25][26][27][28][29][30][31][32] Naturally, scene-depicted environmental conditions can vary with time of day, season, and location.…”

Section: Introductionmentioning

confidence: 99%

SpaceTime Environmental Image Information for Scene Understanding

Tunick¹

2016

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Approved for public release; distribuiton unlimited. ii REPORT DOCUMENTATION PAGEForm Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing the burden, to Department of Defense, Washington Headquarters Services, Directorate for Information Operations and Reports (0704-0188), 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. REPORT DATE (DD-MM-YYYY) April 20162. REPORT TYPE ARL-TR-7652 SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES) 10. SPONSOR/MONITOR'S ACRONYM(S) SPONSOR/MONITOR'S REPORT NUMBER(S) DISTRIBUTION/AVAILABILITY STATEMENTApproved for public release; distribution unlimited. SUPPLEMENTARY NOTES ABSTRACTThe interpretation of space-and time-varying environmental image information can pose serious challenges for rapid and robust scene understanding, particularly for problems of interest to the Army. Changing environmental conditions, such as illumination, precipitation, and vegetation, can obscure features, degrade object recognition, and modify saliency and image context. Current methods for scene understanding do not address elements of image information (and image context) affected by space-and time-changing environmental conditions, and as a result, important and meaningful features of the image data may be overlooked. In this report, we propose that it is important to incorporate space-and time-varying environmental image information from the very beginning of the data collection process so that the recorded images can be more effectively indexed and retrieved for operational use and analysis. This top-down approach not only provides a systematic characterization of the measured data for better scene description, but can also help the end user (Soldier) develop improved course of action strategies based on scene understanding (algorithms and analysis) incorporating battlefield environments changing in space and time.

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Section: Introductionmentioning

confidence: 99%

SpaceTime Environmental Image Information for Scene Understanding

Tunick¹

2016

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“…Other examples of local features are Kernel PCA features [18] and Harris corners [19]. Some systems [20], [21] extend their scope of locality by matching image regions to recognize a location. At this level of representation, the major hurdle lies in achieving reliable segmentation and in robustly characterizing individual regions.…”

Section: Introductionmentioning

confidence: 99%

Biologically-inspired robotics vision monte-carlo localization in the outdoor environment

Siagian

Itti

2007

2007 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-We present a robot localization system using biologically-inspired vision. Our system models two extensively studied human visual capabilities: (1) extracting the "gist" of a scene to produce a coarse localization hypothesis, and (2) refining it by locating salient landmark regions in the scene. Gist is computed here as a holistic statistical signature of the image, yielding abstract scene classification and layout. Saliency is computed as a measure of interest at every image location, efficiently directing the time-consuming landmark identification process towards the most likely candidate locations in the image. The gist and salient landmark features are then further processed using a Monte-Carlo localization algorithm to allow the robot to generate its position. We test the system in three different outdoor environments -building complex (126x180ft. area, 3794 testing images), vegetation-filled park (270x360ft. area, 7196 testing images), and open-field park (450x585ft. area, 8287 testing images) -each with its own challenges. The system is able to localize, on average, within 6.0, 10.73, and 32.24 ft., respectively, even with multiple kidnapped-robot instances.

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“…st , p r j ) < th dist (6) where dist represents the euclidean distance and th dist is fixed to 5 pixels.…”

Section: B) Matching Of the Feature-pointsmentioning

confidence: 99%

“…Authors prefer identifying vertical edges which are precisely referenced in GIS ( [2], [5]). Another way consists on the update of the current view with a set of images, recorded and geo-referenced during a calibration phase ( [6], [11]). …”

Section: Introductionmentioning

confidence: 99%

Reconstruction of the road plane with an embedded stereo-rig in urban environments

Simond

2006 IEEE Intelligent Vehicles Symposium

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Abstract-We present in this article an original method to reconstruct the road in the specific context of urban environment thanks to the data provided by an uncalibrated stereo-vision system. The method consists on extracting then tracking features (points, lines) from the road and estimate the homography induced by the plane between two poses. The purposed method copes with the dense traffic conditions: the free space required (first ten meters in front of the vehicle) is slightly equivalent to the security distance between two vehicles. Experimental issues from real data are presented and discussed.

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A view-based outdoor navigation using object recognition robust to changes of weather and seasons

Cited by 44 publications

References 10 publications

SpaceTime Environmental Image Information for Scene Understanding

SpaceTime Environmental Image Information for Scene Understanding

Biologically-inspired robotics vision monte-carlo localization in the outdoor environment

Reconstruction of the road plane with an embedded stereo-rig in urban environments

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