Uploaded February 2012 | Updated September 2026, 1 week ago
Simultaneous Localization and Mapping (SLAM) is a technique in which the trajectory of a sensor and a map are estimated simultaneously from sensor data. This video demonstrates the 2D SLAM solution developed by CSIRO which enables city-scale mapping in real-time. The solution uses lidar, which is a method of sensing that employs (infrared) laser to measure ranges to surfaces based on time of flight.
Here, two 2D SICK LMS291 lidars are placed on the roof of a vehicle, which is driven around Brisbane, Australia at traffic speeds. There are no other sensors utilized; in particular, no GPS, inertial sensors, or wheel encoders are required. The SICK lidars can measure to a maximum of about 80m, depending on the surface properties.
The local mapping is performed using an EKF-based scan-matching algorithm. Scalability is achieved by using the Atlas framework. Loop closures are detected using a place recognition solution based on regional keypoints extracted from the laser data.
The overall result is map consisting of data collected in multiple datasets over the course of more than one year. The total distance traveled is 165km, and the top speed is 90km/h. The largest loop in the dataset is over 40km. Data collection occurred at various times of day, including peak traffic hours. The appearance of some areas changed dramatically over the data acquisition period due to seasonal variability of vegetation, and major construction on some of the roads.
Relevant Publications (links and pdfs available at http://ict.csiro.au/staff/Robert.Zlot/publications.php ):
[1] https://db.tt/OqZyYWEo or dx.doi.org/10.1016/j.robot.2009.07.009 (pdf)
M. Bosse and R. Zlot, Keypoint Design and Evaluation for Place Recognition in 2D Lidar Maps, Robotics and Autonomous Systems, 57(12), December 2009.
[2] https://db.tt/jdmpM2rC or dx.doi.org/10.1177/0278364908091366 (pdf)
M. Bosse and R. Zlot, Map Matching and Data Association for Large-Scale Two-dimensional Laser-based SLAM, International Journal of Robotics Research, 27(6), June, 2008.
[3] https://db.tt/0hJB9Ouh or dx.doi.org/10.1007/978-3-642-00196-3_42 (pdf)
R. Zlot and M. Bosse, Place Recognition using Keypoint Similarities in 2D Lidar Maps, International Symposium on Experimental Robotics, July, 2008.
More information at:
http://research.ict.csiro.au/research/labs/autonomous-systems/field-robotics/mapping-and-localisation
Simultaneous Localization and Mapping (SLAM) is a technique in which the trajectory of a sensor and a map are estimated simultaneously from sensor data. This video demonstrates the 2D SLAM solution developed by CSIRO which enables city-scale mapping in real-time. The solution uses lidar, which is a method of sensing that employs (infrared) laser to measure ranges to surfaces based on time of flight.
Here, two 2D SICK LMS291 lidars are placed on the roof of a vehicle, which is driven around Brisbane, Australia at traffic speeds. There are no other sensors utilized; in particular, no GPS, inertial sensors, or wheel encoders are required. The SICK lidars can measure to a maximum of about 80m, depending on the surface properties.
The local mapping is performed using an EKF-based scan-matching algorithm. Scalability is achieved by using the Atlas framework. Loop closures are detected using a place recognition solution based on regional keypoints extracted from the laser data.
The overall result is map consisting of data collected in multiple datasets over the course of more than one year. The total distance traveled is 165km, and the top speed is 90km/h. The largest loop in the dataset is over 40km. Data collection occurred at various times of day, including peak traffic hours. The appearance of some areas changed dramatically over the data acquisition period due to seasonal variability of vegetation, and major construction on some of the roads.
Relevant Publications (links and pdfs available at http://ict.csiro.au/staff/Robert.Zlot/publications.php ):
[1] https://db.tt/OqZyYWEo or dx.doi.org/10.1016/j.robot.2009.07.009 (pdf)
M. Bosse and R. Zlot, Keypoint Design and Evaluation for Place Recognition in 2D Lidar Maps, Robotics and Autonomous Systems, 57(12), December 2009.
[2] https://db.tt/jdmpM2rC or dx.doi.org/10.1177/0278364908091366 (pdf)
M. Bosse and R. Zlot, Map Matching and Data Association for Large-Scale Two-dimensional Laser-based SLAM, International Journal of Robotics Research, 27(6), June, 2008.
[3] https://db.tt/0hJB9Ouh or dx.doi.org/10.1007/978-3-642-00196-3_42 (pdf)
R. Zlot and M. Bosse, Place Recognition using Keypoint Similarities in 2D Lidar Maps, International Symposium on Experimental Robotics, July, 2008.
More information at:
http://research.ict.csiro.au/research/labs/autonomous-systems/field-robotics/mapping-and-localisation
![[RA-L/ICRA 2018] Complementary Perception for Handheld SLAM](https://i.ytimg.com/vi/q8NAsqOH2C0/mqdefault.jpg "[RA-L/ICRA 2018] Complementary Perception for Handheld SLAM
We present a novel method for mapping general 3D environments, where sufficient geometric or visual information is not everywhere guaranteed and where the device motion is unconstrained as with handheld systems. The continuous-time SLAM algorithm integrates a lidar, camera and inertial measurement unit in a complementary fashion whereby all sensors contribute constraints to the optimization. The proposed algorithm is designed to expand the domain of mappable environments and therefore increase the reliability and utility of general purpose mobile mapping. A key component of the proposed algorithm is the incorporation of depth uncertainty into visual features, which is effective for noisy surfaces and allows features with and without depth estimates to be modeled in a unified manner. Results demonstrate a wider mappable domain on challenging environments compared to state-of-the-art lidar or vision based localization and mapping algorithms.")
![[RAL2025] Video Foundation Models to Enhance Intermittent Supervision](https://i.ytimg.com/vi/sLUD1xTIeFs/mqdefault.jpg "[RAL2025] Video Foundation Models to Enhance Intermittent Supervision
K. Katuwandeniya, L. Tian and D. Kulić, ‘What Did the Robot Do in My Absence?’ Video Foundation Models to Enhance Intermittent Supervision, in IEEE Robotics and Automation Letters, vol. 10, no. 4, pp. 3222-3229, April 2025, doi: 10.1109/LRA.2025.3539118.
For more information, please check out the project website: https://kavindie.github.io/what-did-the-robot-do-in-my-absence/")
![[IROS2021] CatChatter: Acoustic Perception for Mobile Robots](https://i.ytimg.com/vi/uLSE8__TCHQ/mqdefault.jpg "[IROS2021] CatChatter: Acoustic Perception for Mobile Robots
There are many examples in nature of animals using acoustics to understand and navigate the world around them. Inspired by this, we train an image-to-image translation network to learn the mapping from recorded chirps and echos from an environment to a 360 degree depth map of the environment. This work is focused on expanding on the capabilities of previously published BatVision in a number of ways. We first propose various methods for data augmentation to help the model generalise on less data. We also propose changes to the model architecture to improve performance and training stability. Finally, we investigate the feasibility of 360 degree scene reconstruction by using more microphones and lidar based 3D SLAM data as ground truth for training the model.
You can find the full IEEE RA-L paper here: https://ieeexplore.ieee.org/document/9472944")
