Study On The Key Questions Of Mobile Robot Navigation In Unknown Indoor Environments

Mobile robot is a kind of robot which works in complicated environments and has an ability of self-programming, self-organizing and self-adapting. Mobile robot navigation in unknown environment is the bottleneck of using mobile robot broadly. The two key questions of navigation are building map in real time and obtaining robot's pose precisely. This paper mainly focuses on mobile robot navigation in indoor environment where neither the pose of the robot nor the environment information is known, i.e. the so called Simultaneous Localization and Mapping (SLAM) problem.Firstly this paper introduces the two key techniques of SLAM, one of which is representative expression means of environmental map, and another is typical localization methods of mobile robots. On the basis of introducing the characteristic of sonar, the process of building grid map using Bayesian rule is presented. In order to increase the precision of the built map, Hough transform is applied. Particle filter is used to localize robot and its algorithm is explained. A histogram matching is utilized in the sensor-data-update phase of particle filter localization. Experiments show it makes the algorithm more efficient and robust.To reduce the complexity of data association and enhance the real-time performance of the algorithm, an improved SLAM algorithm is put forward using Bayesian gird map and partial filter. This algorithm reduces the association between the precision of map and that of robot pose. Particles are distributed only around the robot, so it promotes the real-time performance. Simulation and experiments results validate the feasibility of algorithm.