In this paper, we propose a multi-object tracking and reconstruction approach through measurement-level fusion of LiDAR and camera. The proposed method, regardless of object class, estimates 3D motion and structure for all rigid obstacles. Using an intermediate surface representation, measurements from both sensors are processed within a joint framework. We combine optical flow, surface reconstruction, and point-to-surface terms in a tightly-coupled non-linear energy function, which is minimized using Iterative Reweighted Least Squares (IRLS). We demonstrate the performance of our model on different datasets (KITTI with Velodyne HDL-64E and our collected data with 4-layer ScaLa Ibeo), and show an improvement in velocity error and crispness over state-of-theart trackers.

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Inspired by the ideas behind superpixels, which segment an image into homogenous regions to accelerate subsequent processing steps (e.g. tracking), we present a sensorfusion-based segmentation approach that generates dense depth regions referred to as supersurfaces. This method aggregates both a point cloud from a LiDAR and an image from a camera to provide an over-segmentation of the three-dimensional scene into piece-wise planar surfaces by utilizing a multi-label Markov Random Field (MRF). A comparison between this method that generates supersurfaces, image-based superpixels, and RGBDbased segments using a subset of KITTI dataset is provided in the experimental results. We observed that supersurfaces are less redundant and more accurate in terms of average boundary recall for a fixed number of segments.

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In this paper, after studying the demands and requirements of a perfect smart grid and specifications of network topology and sensor nodes utilized in a Wireless Sensor Network [WSN], a manipulated version of them is proposed to be combined with smart grid in order to improve the data acquisition step, increase the security and facilitate the process of information and desired commands from users. We will take the advantage of computer simulations to illustrate improved capacity and speed of this method. Finally, we will analyze the advantages of employing this proposed structure in a smart grid.