Lidar is extensively used in the industry and mass market, due to its measurement accuracy and insensitivity to brightness compared to cameras. It is applied onto a broad range of applications, like geodetic engineering, self driving cars or virtual reality. But the 3D Lidar with multiple beams is very expensive, and the massive measurements data can not be fully processed timely. The purpose of this paper is to explore the possibility of using cheap 2D Lidar off-the-shelf, for 3D reconstruction with high accuracy. The 3D map quality is evaluated by our proposed metrics in this paper lastly. The 3D map is constructed in two ways, one way in which the scan is performed at known positions. The other way, in which the 2D Lidar for mapping and the localization part are placed on a trolley, then the trolley is pushed on the ground. The generated maps by these approaches are converted to octomaps before the evaluation. The similarity and difference between the two maps will be evaluated by these metrics in a normalized value ranging from 0 to 1. The established mapping system is composed of several modular components. A 3D bracket is designed for assembling the Lidar with a long range, the driver and the motor. A cover platform is designed for the imu and Lidar with a shorter range but high accuracy, which will be used to implement 2D SLAM. The software is stacked up in different packages on ROS.
Three-dimensional models provide a volumetric representation of space which is important for a variety of robotic applications including flying robots and robots that are equipped with manipulators. In this paper, we present an open-source framework to generate volumetric 3D environment models. Our mapping approach is based on octrees and uses probabilistic occupancy estimation. It explicitly represents not only occupied space, but also free and unknown areas. Furthermore, we propose an octree map compression method that keeps the 3D models compact. Our framework is available as an open-source C++ library and has already been successfully applied in several robotics projects. We present a series of experimental results carried out with real robots and on publicly available real-world datasets. The results demonstrate that our approach is able to update the representation efficiently and models the data consistently while keeping the memory requirement at a minimum. A probabilistic model allows us to reason about the world and make statistically optimal decisions using Bayesian decision theory. However, in practice the intractability of the decision problem forces us to adopt simplistic loss functions such as the 0 1 loss or Hamming loss and as result we make poor decisions through MAP estimates or through low-order marginal statistics. In this work we investigate optimal decision making for more realistic loss functions. Specifically we consider the popular intersection-over-union (IoU) score used in image segmentation benchmarks and show that it results in a hard combinatorial decision problem. To make this problem tractable we propose a statistical approximation to the objective function, as well as an approximate algorithm based on parametric linear programming. We apply the algorithm on three benchmark datasets and obtain improved intersection-over-union scores compared to maximum-posterior-marginal decisions. Our work points out the difficulties of using realistic loss functions with probabilistic computer vision models. Most current navigation algorithms in mobile robotics produce 2D maps from data provided by 2D sensors. In large part this is due to the availability of suitable 3D sensors and difficulties of managing the large amount of data supplied by 3D sensors. This paper presents a novel, multi-resolution algorithm that aligns 3D range data stored in occupied voxel lists so as to facilitate the construction of 3D maps. Multi-resolution occupied voxel lists (MROL) are voxel based data structures that efficiently represent 3D scan and map information. The process described in this research can align a sequence of scans to produce maps and localise a range sensor within a prior global map. An office environment (200 square metres) is mapped in 3D at 0.02 m resolution, resulting in a 200,000 voxel occupied voxel list. Global localisation within this map, with no prior pose estimate, is completed in 5 seconds on a 2 GHz processor. The MROL based sequential scan matching is compared to a standard iterative closest point (ICP) implementation with an error in the initial pose estimate of plus or minus 1 m and 90 degrees. MROL correctly scan matches 94 of scans to within 0.1 m as opposed to ICP with 30 within 0.1 m. In previous work, we proposed a unique landmark-based map learning method for mobile robots based on the "co-visibility" information i.e., very coarse qualitative information on "whether two objects are visible together or not". In this paper, we introduce two major enhancements to this method: (1) automatic optimization of distance estimation function, and (2) weighting of observation information based on reliability. Simulation results show that these enhancements improve the performance of this proposed method dramatically, not only in the qualitative accuracy measure, but also in the quantitative measure.