Clement Deymier

Fast calibration of embedded non-overlapping cameras

This article deals with a simple and flexible extrinsic calibration method, for non-overlapping camera rig. The cameras do not see the same area at the same time. They are rigidly linked and can be moved. The most representative application is the mobile robotics domain. The calibration procedure consists in maneuvering the system while each camera observes a static scene. A linear solution derived from hand eye calibration scheme is proposed to compute an initial estimate of the extrinsic parameters. The main contribution is a specific bundle adjustment which refines both the scene geometry and the extrinsic parameters. Finally, an efficient implementation of the specific bundle adjustment step is defined for online calibration purpose. The proposed approach is validated with both synthetic and real data.

IPCC algorithm: Moving object detection in 3D-Lidar and camera data

In this paper, we present a new algorithm named IPCC for Iterative Photo Consistency Check. The goal of this one is to detect a posteriori moving object in both camera and range data. The range data may be provided by different sensor such as: Riegl, Kinect or Velodyne with no distinction. The key idea is to consider that range data acquired on static objects are photo-consistent, they have the same color and texture in all the camera images, but range data acquired on moving object are not photo-consistent. The main problem is to take in account that range finding sensor and camera are not synchronous, so what is seen in camera is not what range finding sensors acquires. The major contribution in this paper is an original way to find non photo-consistent range data using the camera images in an erosion process of the scene. Experiments show the relevance of the proposed method in terms of both accuracy and computation time.

Probabilistic Block-Matching based 6D camera localization

This paper describes a full 6D localization algorithm based on probabilistic motion field. The motion field is obtained by an adaptation of the video compression algorithm known as Block-Matching which provides a sparse optical flow. Such a technique is very fast and allows real time applications. Image is decomposed in a grid of rectangular blocks. For each block, a relative displacement between consecutive images is calculated. Obtained motion flow is analyzed probabilistically in order to extract for each movement detection its uncertainty and to obtain subpixelic information about the area movement. Such motion flow is then used in order to obtain full 6 degrees of freedom camera localization using epipolar geometry based techniques without any 3D landmark reconstruction requirement. The method is applied to real data set obtained from a mobile robot and compared with SIFT and Harris detection.

Non-parametric occupancy map using millions of range data

This paper presents a fast method to estimate the probability of occupancy of a space point from a huge set of 3D rays represented in a common reference. These data can come from any range finding sensor such as : Lidar, Kinect or Velodyne. The key idea is to consider that the occupancy of a space 3D point is linked to 1) the number of 3D point belonging to a local volume around the point and 2) the number of rays crossing through the same volume. We propose a probabilistic non-parametric framework based on KNN estimator. The major contribution of the paper is an original solution to search rays in the neighborhood of a 3D point with a five dimensional binary tree that can handle several millions measurements. Experiments shows the relevance of the proposed method in terms of both accuracy and computation time. Moreover, the resulting method has been applied to three different 3D sensors: a Kinect, a 3D Lidar (Velodyne HDL-64E) and a mono-planar Lidar.

Improved visual localization and navigation using proprioceptive sensors

We address the problem of vehicle (mobile robot) navigation by combining visual-based reconstruction and localization with metrical information given by the proprioceptive sensors such as the odometry sensor. The proposed approach extends the navigation system based on a monocular vision [1] which is able to build a map and localize the vehicle in the real time way using only one camera. An extended kalman filter is used to integrate odometric information to estimate the vehicle position. This position is updated by the localization obtained from the vision system. Experimental result carried out with an urban electric vehicle will show the improvement of the navigation system and its robustness to the temporary loss of images.