We demonstrate the use of semantic object detections as robust features for Visual Teach and Repeat (VTR). Recent CNN-based object detectors are able to reliably detect objects of tens or hundreds of categories in a video at frame rates. We show that such detections are repeatable enough to use as landmarks for VTR, without any low-level image features. Since object detections are highly invariant to lighting and surface appearance changes, our VTR can cope with global lighting changes and local movements of the landmark objects. In the teaching phase, we build a series of compact scene descriptors: a list of detected object labels and their image-plane locations. In the repeating phase, we use Seq-SLAM-like relocalization to identify the most similar learned scene, then use a motion control algorithm based on the funnel lane theory to navigate the robot along the previously piloted trajectory. We evaluate the method on a commodity UAV, examining the robustness of the algorithm to new viewpoints, lighting conditions, and movements of landmark objects. The results suggest that semantic object features could be useful due to their invariance to superficial appearance changes compared to low-level image features.
This paper applies an existing vision-based navigation algorithm to a micro aerial vehicle (MAV). The algorithm has previously been used for long-range navigation of ground robots based on on-board 3D vision sensors such as a stereo or Kinect cameras. A teach-and-repeat operational strategy enables a robot to autonomously repeat a manually taught route without relying on an external positioning system such as GPS. For MAVs we show that a monocular downward looking camera combined with an altitude sensor can be used as 3D vision sensor replacing other resource-expensive 3D vision solutions. The paper also includes a simple path tracking controller that uses feedback from the visual and inertial sensors to guide the vehicle along a straight and level path. Preliminary experimental results demonstrate reliable, accurate and fully autonomous flight of an 8-m-long (straight and level) route, which was taught with the quadrocopter fixed to a cart. Finally, we present the successful flight of a more complex, 16-m-long route. The objective of this paper is to develop a vision-based navigation technique for micro aerial vehicles, quadrotor type, to operate in GPS-denied environment. The navigation method has been developed while using appearance-based Visual-Teach-and-Repeat (VT&R) technique. In a teaching phase, a quadrotor is manually navigated along a desired route to collect a set of reference images. In a repeating phase, the quadrotor is able to autonomously follow the desired route using these reference images. Self-localization is developed to determine the current segment of the desired route by a number of Speeded-Up Robust Features (SURF), matched between the current image and the reference images. In this paper, three methods of self-localization are proposed and compared. After performing self-localization, the quadrotor computes appearance-based motion control commands (desired yaw and height) for the next movement in order to keep track of the desired route. This computation is developed on Funnel Lane theory, which was originally proposed in Chen and Birchfield (IEEE Trans. Robot. 25(3), 749---754 (11)) for 2D navigation of a ground vehicle. The paper extends this theory to 3D navigation of a quadrotor.The proposed self-localization methods are tested with several image databases. Finally, an online experiment of proposed VT&R technique is demonstrated using Ar.Drone quadrotor model.  This paper describes a system built to enable long-range rover autonomy using a stereo camera as the only sensor. During a learning phase, the system builds a manifold map of overlapping submaps as it is piloted along a route. The map is then used for localization as the rover repeats the route autonomously. The use of local submaps allows the rover to faithfully repeat long routes without the need for an accurate global reconstruction. Path following over nonplanar terrain is handled by performing localization in three dimensions and then projecting this down to a local ground plane associated with the current submap to perform path tracking. We have tested this system in an urban area and in a planetary analog setting in the Canadian High Arctic. More than 32 km was covered—99.6p autonomously—with autonomous runs ranging from 45 m to 3.2 km, all without the use of the global positioning system (GPS). Because it enables long-range autonomous behavior in a single command cycle, visual teach and repeat is well suited to planetary applications, such as Mars sample return, in which no GPS is available. © 2010 Wiley Periodicals, Inc.