Salient Feature Detection for 3D LIDAR Registration

In this paper we propose a novel detection algorithm SKIP-3D (SKeleton Interest Point) for extraction of edges from multi-layer LIDAR scans. SKIP-3D exploits the organization of LIDAR measurements to search silent points in each layer through an iterative bottom-up procedure, removing low curvature points. The edge features from two point clouds are associated and used for their alignment. The experimental results shows that the proposed approach is efficient and reliable.https://youtu.be/4l4ynkzqqr

Bowling for Calibration: An Undemanding Camera Calibration Procedure Using a Sphere

Camera calibration is a critical problem in computer vision. This paper presents a new method for extrinsic parameters computation: images of a ball rolling on a flat plane in front of the camera are used to compute roll and pitch angles. The calibration is achieved by an iterative Inverse Perspective Mapping (IPM) process that uses an estimation on ball gradient invariant as a stop condition. The method is quick and as easy to use as throw a ball and is particularly suited to be used to quickly calibrate vision systems in unfriendly environments where a grid is not available. The algorithm correctness is demonstrated and its accuracy is computed using both computer generated and real images

Efficient calibration of four wheel industrial AGVs

In this paper, we propose a novel method for extrinsic and intrinsic automatic calibration of four wheel industrial Automated Guided Vehicles (AGVs) compliant with Ackermann and Dual Drive kinematics. For each kinematic model the algorithm estimates the trajectories measured by an on-board sensor and the expected ones given the state of the wheels. The estimation exploits the model equations derived in this work which constrain calibration parameters and measurements from wheel encoders and sensor odometry. The parameter values are computed through closed-form solutions of least-square estimation. The method has been implemented on Programmable Logic Controllers and tested on industrial AGVs. The developed procedure computes the parameters in about 10−15 minutes, a significant improvement compared with one hour or more required by manual AGV calibration. Experiments with AGVs of various sizes in a warehouse have assessed the accuracy and stability of the proposed approach. The position accuracy achieved by AGVs calibrated with the proposed method is higher than the one achieved by manual calibration

Bowling for Calibration: An Undemanding Camera Calibration Procedure Using a Sphere

Camera calibration is a critical problem in computer vision. This paper presents a new method for extrinsic parameters computation: images of a ball rolling on a flat plane in front of the camera are used to compute roll and pitch angles. The calibration is achieved by an iterative Inverse Perspective Mapping (IPM) process that uses an estimation on ball gradient invariant as a stop condition. The method is quick and as easy to use as throw a ball and is particularly suited to be used to quickly calibrate vision systems in unfriendly environments where a grid is not available. The algorithm correctness is demonstrated and its accuracy is computed using both computer generated and real images