# Vision-Guided Six-Legged Walking of Little Crabster Using a Kinect Sensor
## 1 Introduction
Recently, several walking methods for legged robots with vision systems were proposed to compensate for the aforementioned disadvantage. [PERSON] et al. used stereo vision to implement 2D localization of a humanoid robot, H7, and performed biped walking while avoiding obstacles on the walking path [10]. [PERSON] et al. developed a navigation algorithm for mobile robots in unknown rough terrain [11]. Solely relying on stereo images from robot-fixed cameras, the algorithm uses visual odometry for localization. The authors validated the concept with a wheeled robot and a six-legged robot. [PERSON] et al. determined six DOF poses of a six-legged robot with a monocular vision system using the Parallel Tracking and Mapping (PTAM) algorithm and Inertial Measurement Unit (IMU). They used a self-localization system together with an RRT-based motion planner, which allows a robot to walk autonomously on unknown rough terrain [12]. Many researchers have studied vision system-based walking robots; however, the majority of studies were restricted to localization in generated maps and walking path planning for avoiding obstacles. Furthermore, because those studies were mainly focused on obstacle avoidance and path planning, without considering the ground shape, they did not attempt to improve dynamic walking stability, which is typically defined as how stably the robot is able to walk at high walking speeds. More recently, an attempt was made to use ground shape information from a depth camera in online walking pattern generation. [PERSON] et al. developed a stereo vision near-field terrain mapping system for the Legged Squad Support System (LS3) to automatically adjust robot gait in complex natural terrain [13]. [PERSON] et al. proposed a method for stable biped walking on rough terrain using inverse dynamics control and stereo vision information [14]. They successfully implemented a stable 3D dynamic walking simulation by using the ground shape information obtained from a depth camera attached to the robot head. As the above works show, only a few studies of vision-guided walking algorithms for improving walking stability were experimentally performed [15; 16]. While employing vision sensors and tactile sensors, these recent works did not consider the safety and reliability simultaneously in determining the landing positions [17; 18]. In this paper, we developed a vision-guided walking algorithm for a real six-legged robot. The main contributions of this work are (1) a spatial noise-filtering method of 3D ground information using a cost-effective Kinect sensor, (2) a landing position determination algorithm considering safety and reliability, and (3) a ground merging algorithm between successive ground data using the overlapped area. The foot landing positions can be adjusted according to the ground shape information, which was obtained from a Kinect sensor. In addition, we achieved highly stable six-legged walking by applying a balance control algorithm previously developed by the author [19]. A newly-developed special calibration tool can help us to derive a coordinate transformation relationship between the camera coordinate system and the robot-fixed coordinate system. The proposed algorithm can adjust the foot landing position by considering the reliability and safety of the landing area by merging 3D ground shape data obtained from the Kinect sensor. We validate the proposed vision-guided six-legged walking algorithm through walking experiments of a six-legged robot, LCR 200.