![occupancy grid mapping with ultrasonic range finder occupancy grid mapping with ultrasonic range finder](https://www.researchgate.net/publication/347478827/figure/fig1/AS:1002382049689603@1615997912729/Example-of-likelihood-field-a-Original-map-occupancy-grid-map-where-the-color.png)
Slow localization wasn't an issue for the environment I was using the robot in. So YES this was a success for my project. If your robot doesn't need to respond quickly, it's better to use a coordinate transform snapshotted during the middle of the buffering operation, not at the end. Your localization will suffer from the extra delay due to buffering the sonar. (Also, since I was doing this on an arduino, it was a great way to drop the floating point numbers and use raw bytes instead) I had good results with only 2.5cm pixels and large. To get it to work, I had to buffer it for ~0.5s (thus creating a "laserscan" of about 25 data points) - it will not work very well if you publish single- or two-point "laserscans" This is extremely important! My implementation used two sonar sensors that swept side to side. Stagger your sonar sensor's phase, especially if the sensors are looking in the same direction (or opposite by 180degrees). I made a small differential-drive bot that mapped using sonar, wheel odometry, and inertial sensors. Laser_ndTransform(tf::StampedTransform(tf::Transform(tf::createQuaternionFromRPY(0, 0, degree_to_radian(ROS_FrontSonarAngle)), Scan.time_increment = (1 / laser_frequency) / (num_readings) įor(int i=1 iSonarMeasurements/100.0 // Convert to Meters Scan.angle_increment = 3.14 / num_readings Laser_pub = n.advertise("sonar_laser_scan", 1000) N.param("publish_laser", publish_laser, publish_laser) Tf::TransformBroadcaster laser_broadcaster
![occupancy grid mapping with ultrasonic range finder occupancy grid mapping with ultrasonic range finder](https://www.mdpi.com/sensors/sensors-22-00305/article_deploy/html/images/sensors-22-00305-g027.png)
#OCCUPANCY GRID MAPPING WITH ULTRASONIC RANGE FINDER CODE#
If so I'd like to know what map type setup etc you used.Ĭurrently the map is a mess (An only home brew solution works just fine) so I'm sure it must be possible.įYI the Sonar data is being published as both Range and laser_scan message types.ĮDIT - Sonar Laser Code for benefit of others: bool ToeminatorROSBridge::publishSonarAsLaserData(ros::Time time, RobotData* pRobotData) Experimentally, a range accuracy of < 1.7 mm (1σ) was achieved on a 1 × 2 m sample using miniaturised EMATs operating at a wavelength of 22 mm.Had anyone had any success creating a reasonable map in ROS with just sonar and odometry data?
![occupancy grid mapping with ultrasonic range finder occupancy grid mapping with ultrasonic range finder](https://www.mdpi.com/sensors/sensors-22-00305/article_deploy/html/images/sensors-22-00305-g033.png)
It is shown that the proposed mapping algorithm successfully estimates the position of a sample's edges. The principle is demonstrated in both simulation and laboratory-based experiments. A Bayesian mapping technique (Occupancy grid mapping) was used to map the boundaries of an irregular sample in a pseudo-pulse-echo mode. Shear Horizontal (SH) guided waves generated by Electro-Magnetic Acoustic Transducers (EMATs) are used for mapping steel samples with a nominal thickness of 10 mm. It considers the specific problem of mapping geometric features using the guided ultrasonic waves, which enables the localisation of edges and/or the welded joints. Experimentally, a range accuracy of < 1.7 mm (1σ) was achieved on a 1 × 2 m sample using miniaturised EMATs operating at a wavelength of 22 mm.ĪB - This paper evaluates the benefits of using ultrasonic guided waves for the mapping of a structure, when implemented on a mobile magnetic robotic platform. N2 - This paper evaluates the benefits of using ultrasonic guided waves for the mapping of a structure, when implemented on a mobile magnetic robotic platform. T1 - Application of ultrasonic guided waves to robotic occupancy grid mapping