11 research outputs found
Toward 3D reconstruction of outdoor scenes using an MMW radar and a monocular vision sensor
International audienceIn this paper, we introduce a geometric method for 3D reconstruction of the exterior environment using a panoramic microwave radar and a camera. We rely on the complementarity of these two sensors considering the robustness to the environmental conditions and depth detection ability of the radar, on the one hand, and the high spatial resolution of a vision sensor, on the other. Firstly, geometric modeling of each sensor and of the entire system is presented. Secondly, we address the global calibration problem, which consists of finding the exact transformation between the sensors' coordinate systems. Two implementation methods are proposed and compared, based on the optimization of a non-linear criterion obtained from a set of radar-to-image target correspondences. Unlike existing methods, no special configuration of the 3D points is required for calibration. This makes the methods flexible and easy to use by a non-expert operator. Finally, we present a very simple, yet robust 3D reconstruction method based on the sensors' geometry. This method enables one to reconstruct observed features in 3D using one acquisition (static sensor), which is not always met in the state of the art for outdoor scene reconstruction.The proposed methods have been validated with synthetic and real data
Direct Sparse Odometry with Rolling Shutter
Neglecting the effects of rolling-shutter cameras for visual odometry (VO)
severely degrades accuracy and robustness. In this paper, we propose a novel
direct monocular VO method that incorporates a rolling-shutter model. Our
approach extends direct sparse odometry which performs direct bundle adjustment
of a set of recent keyframe poses and the depths of a sparse set of image
points. We estimate the velocity at each keyframe and impose a
constant-velocity prior for the optimization. In this way, we obtain a near
real-time, accurate direct VO method. Our approach achieves improved results on
challenging rolling-shutter sequences over state-of-the-art global-shutter VO
Indoor autonomous navigation using visual memory and pattern tracking
The paper deals with autonomous environment mapping, localisation and navigation using exclusively monocular vision and multiple 2D pattern tracking. The environment map is a mosaic of 2D patterns detected on the ceiling plane and used as natural landmarks. The robot is able to reproduce learned trajectories defined by key images representing the visual memory. The pattern tracker is based on particle filetring. It uses both image contours and gray scale level variations to track efficiently 2D patterns on cluttered background. An original observation model used for filter state updating is presented.
Autonomous image based navigation in indoor environment
International audienc