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visual_odometry is an application available in ccny_rgbd for estimating the motion of a moving RGBD camera. It requires a stream of registered depth and rgb images, as well as a CameraInfo matrix. These topics can be generated using rgbd_image_proc in conjunctions with ccny_openni_launch, or any compatible driver.

visual_odometry is a real-time application with relatively low computational requirements. It does not output a map, and does not perform SLAM in the sense of closing large loops in the trajectory. For a graph-based SLAM solution, visual_odometry can be used in conjuntion with keyframe_mapper.

ROS API

Subscribed Topics

/rgbd/rgb (sensor_msgs/Image)

• Input RGB image

/rgbd/depth (sensor_msgs/Image)

• Input Depth image (registered in the RGB frame)

/rgbd/info (sensor_msgs/CameraInfo)

• CameraInfo, applies to both images.

Published Topics

odom (nav_msgs/Odometry)

• Pose of the base frame in relation to the fixed frame, as an odometry message.

feature/cloud (sensor_msgs/PointCloud2)

• Point cloud of the detected features

model/cloud (sensor_msgs/PointCloud2)

• Point cloud of the sparse model maintained for registration.

model/covariances (visualization_msgs/Marker)

• Covariance matrices of the sparse model features. The 3x3 cov. matrix is visualized by the 3 principal components, each with a length of +/- 3 sigmas.

Parameters

General parameters

~queue_size (int, default: 5)

• input and output queue sizes

~base_frame (string, default: "camera_link")

• The moving frame

~fixed_frame (string, default: "odom")

• The fixed frame

~publish_tf (bool, default: true)

• Whether to publish a tf transform between the fixed and moving frames.

Feature parameters

~feature/detector_type (string, default: "GFT")

• Type of feature detector. Available types: "GFT", "SURF", "STAR", "ORB"

~feature/publish_cloud (bool, default: false)

• Whether to publish the point cloud of 3D features

~feature/smooth (int, default: 0)

• Half-size of the window for the Gaussian smoothing kernel. 0 = no smoothing

~feature/max_range (double, default: 5.5)

• Maximum z-range, in meters, allowed for valid features

~feature/GFT/n_features (int, default: 400)

• (GFT detector only) Number of desired features

~feature/GFT/min_distance (int, default: 1)

• (GFT detector only) Minimum distance, in pixels, between detected features

~feature/ORB/n_features (int, default: 400)

• (ORB detector only) Number of desired features

~feature/ORB/threshold (double, default: 31.0)

• (ORB detector only) Detection threshold

~feature/STAR/min_distance (int, default: 2)

• (STAR detector only) Minimum distance, in pixels, between detected features

~feature/STAR/threshold (double, default: 32.0)

• (STAR detector only) Detection threshold

~feature/SURF/threshold (double, default: 400.0)

• (SURF detector only) Detection threshold

Registration parameters

~reg/reg_type (string, default: "ICPProbModel")

• Registration type. Available options: "ICPProbModel", "ICP" (deprecated)

~reg/motion_constraint (int, default: 0)

• Dimensions which are constrained (no motion allowed). NONE = 0, ROLL_PITCH = 1, ROLL_PITCH_Z = 2. A ground robot, for example, will usually have ROLL_PITCH_Z constraints.

~reg/ICPProbModel/max_iterations (int, default: 10)

• Maximum ICP iterations convergence criteria for ICP

~reg/ICPProbModel/min_correspondences (int, default: 15)

• Minimum required correspondences for ICP

~reg/ICPProbModel/max_model_size (int, default: 3000)

• Maximum number of features in the sparse model. Once saturated, new points will overwrite old points.

~reg/ICPProbModel/max_corresp_dist_eucl (double, default: 0.15)

• Maximum distance (in meters) for ICP correspondences

~reg/ICPProbModel/max_assoc_dist_mah (double, default: 10.0)

• Maximum Mahalanobis distance (unitless) for feature-to-model association in Kalman Filter

~reg/ICPProbModel/n_nearest_neighbors (int, default: 4)

• Number of Euclidean candidates to consider for finding best Mahalanobis nearest neighbor for Kalman Filter

~reg/ICPProbModel/publish_model_cloud (bool, default: false)

• Whether to publish the sparse model as a point cloud

~reg/ICPProbModel/publish_model_covariance (bool, default: false)

• Whether to publish the sparse model covariances as a visualization markers. Markers will display the 3 principal axes of the 3x3 covariance matrix, with a length of +/- 3 sigmas

~reg/ICPProbModel/tf_epsilon_linear (double, default: 1e-4)

• Linear convergence criteria for ICP

~reg/ICPProbModel/tf_epsilon_angular (double, default: 1.7e-3)

• Angular convergence criteria for ICP

Transforms

Required tf Transforms

camera_link → [incoming image frame]

• Transform from the moving frame to the optical camera frame.

Provided tf Transforms

odom → camera_link

• The pose of the moving frame in the fixed frame. Only provided when publish_tf is enabled.

Services

save_sparse_map (ccny_rgbd/Save)

• Saves the sparse feature model as a pcd point cloud.

Usage

First, launch the device and image processing via ccny_openni_launch.

Note: You can use any launcher, as long as it outputs a registered depth and rgb image, and the corresponding CameraInfo (see API above).

roslaunch ccny_openni_launch openni.launch 

Next, launch the visual_odometry.launch:

roslaunch ccny_rgbd visual_odometry.launch