Line Feature Map Building For Unknown Indoor Environments

Robotic mapping is an essential issue in robotics and artificial intelligent.It acts as the base of reconnaissance,search,rescue,navigation et al for mobile robot working in unknown environment.Thus its research and application is much significant to improve the intelligence of robot and promote the process that robots service for human in daily life.The main challenges of robotic mapping exist on SLAM(Simultaneous Localization and Mapping),high dimensionality,data association,dynamic and loop features in environment and autonomous exploration.This dissertation addresses to robotic mapping in unknown indoor environments.Considering the structure characteristic of indoor environment,we adopt line that describes the profile of obstacle as feature.With the research on information process,SLAM and exploration,an integrated map building approach is established.In addition, to boost the mobility of the robot for map bilding in the narrow environment,the motion modeling and optimal control of omni-directional mobile robots isstudied, which provides a base for future work.The whole paper includes the following detail:(1) A combining method of Hough transform,coincided-line detecting and Least Square is presented and used to fit line segment from measurement.This method boosts the fitting precision,thus the environment can be described accurately and succinctly.(2) We proposed an incremental SLAM approach based on the best congruence between dot data in the current measurement and line segments in the previously-built map.Each iteration of SLAM consists of 3 stages:local map building,robot pose estimating,and map integrating.In pose estimating,least square method is used iteratively to obtain the best correspondence between the measurement and the half-baked map.Removing improper match and defining weighted matrix are both implemented to reduce the errors of measurement,line fitting and previously-built map.In map integrating,with the pose estimated,the map is updated by fusing the local map built from current measurement and the previously-built map.This method avoids the hypothesis of Gauss noise as EFK-SLAM and reduces the sensitivity to the error of data corresponding.It can work online with a low computation load in match.Experimental results with real data demonstrate the approach is effective and robust for indoor environment mapping.(3) We further proposed an approach called FastLineSLAM by introducing the incremental SLAM algorithm based on dot-line congruence into particle filter. In the approach,each particle carries an assumption on robot path and employs the SLAM algorithm based on dot-line congruence to update the map. Both the motion and the observation information are considered in the importance function by using the dot-line congruence method to estimate the pose of robot.The weight of the particle is updated according to the congruence between current measurement and segment features in previously-built map.The wrong particles resulted from mis-matching or error accumulation are filtered with selective resampling.Experimental results with real data demonstrate the approach is effective and robust for mapping dynamic and loop indoor environment by solving the residual error existing in the incremental SLAM algorithm based on dot-line congruence.Both of the particle number and memory are quite lower than the existing mapping methods using particle filter.(4) We presented a line-feature-guided exploration approach to find the next best pose of view.The candidates of view are not only generated from line features but also inherited the orientation property of line features,which is used to guide the exploration.The environment is divided into a sequence of exploration space which is defined as a subspace with an exploration direction and a start position.According to affiliation between candidates and exploration spaces,an heuristic NBV search is implemented by obeying the direction guide of both the candidate and the exploration space.Experimental results demonstrate the proposed approach ensures the localization of candidates of view and is efficient for active map building in indoor environment to get a good complete coverage with few criss-crossing motion.(5) We studied the motion control and path planning of the omni-directional mobile robot.Through analysis the characteristic of the kinematics and dynamics of the omni-directional robot which equipped four omni-directional wheels,its motion control model is provided.Then the model is simplified rationally according to the feature of model equations,which reduces the cost of computation effectively.Using Bang-Bang control,time-optimal trajectory generation method also is carried out and produced a real-time effect integrated with the motion control model.The effectiveness of the method has been demonstrated by experiments.