Mobile robots navigating in indoor and outdoor environments must be able to identify and avoid unsafe terrain. Although a significant amount of work has been done on the detection of standing obstacles (solid obstructions), not much work has been done on the detection of negative obstacles (e.g. dropoffs, ledges, downward stairs). We propose a method of terrain safety segmentation using deep convolutional networks. Our custom semantic segmentation architecture uses a single camera as input and creates a freespace map distinguishing safe terrain and obstacles. We then show how this freespace map can be used for real-time navigation on an indoor robot. The results show that our system generalizes well, is suitable for real-time operation, and runs at around 55 fps on a small indoor robot powered by a low-power embedded GPU.
@cite_21: This tutorial chapter aims to teach the main theoretical concepts and explain the use of ROS Navigation Stack. This is a powerful toolbox to path planning and Simultaneous Localization And Mapping (SLAM) but its application is not trivial due to lack of comprehension of the related concepts. This chapter will present the theory inside this stack and explain in an easy way how to perform SLAM in any robot. Step by step guides, example codes explained (line by line) and also real robot testing will be available. We will present the requisites and the how-to’s that will make the readers able to set the odometry, establish reference frames and its transformations, configure perception sensors, tune the navigation controllers and plan the path on their own virtual or real robots.
@cite_2: This contribution presents a novel approach for efficient online planning of topologically distinctive mobile robot trajectories. Trajectory optimization deforms an initial coarse path drafted by a global planner with respect to robot motion related objectives and constraints. The primary objective is to reach a goal state in minimal time on a collision free path that adheres to the kinematic and dynamic constraints of the mobile robot. Conventional local planners get often stuck in a local optimal trajectory as they are unable to transit across obstacles. Our approach seeks the globally optimal trajectory as it maintains and optimizes a subset of admissible candidate trajectories of distinctive topologies in parallel. In case of dynamic environments the planner switches to the current globally optimal trajectory among the candidate set. The online trajectory planning with timed elastic bands is tightly integrated with the robot motion feedback control. The comparative analysis with conventional local planners confirms the advantages of maintaining distinctive topologies to circumnavigate dynamic obstacles.
@cite_8: 
In the context of indoor and outdoor robot navigation, most development has been based on the use of LIDAR or depth-sensing cameras for localization and avoidance @cite_8 @cite_2 . These approaches create a pointcloud from data captured by the LIDAR or depth camera. This pointcloud is then used to build a costmap @cite_21 , which stores locations of obstacles and free space. Pathfinding algorithms can be used on this costmap to find a safe path for a robot to travel.