This paper addresses the issues of unstructured terrain modeling for the purpose of navigation with legged robots. We present an improved elevation grid concept adopted to the specific requirements of a small legged robot with limited perceptual capabilities. We propose an extension of the elevation grid update mechanism by incorporating a formal treatment of the spatial uncertainty. Moreover, this paper presents uncertainty models for a structured light RGB-D sensor and a stereo vision camera used to produce a dense depth map. The model for the uncertainty of the stereo vision camera is based on uncertainty propagation from calibration, through undistortion and rectification algorithms, allowing calculation of the uncertainty of measured 3D point coordinates. The proposed uncertainty models were used for the construction of a terrain elevation map using the Videre Design STOC stereo vision camera and Kinect-like range sensors. We provide experimental verification of the proposed mapping method, and a comparison with another recently published terrain mapping method for walking robots.
This paper presents an adaptive foothold planning method for a hexapod walking robot. A local terrain map acquired with an inexpensive structured light sensor is exploited as the information source for the planning algorithm, which uses a polynomial-based approximation method to create a decision surface. The robot learns from simulations, therefore no a priori knowledge is required. The results show that the method is general enough to work on various types of terrain. The planned footholds enable the robot to walk more stable, avoiding slippages and fall-downs.
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