Thank you very much for the explanation! This intricacy of sensor models was something I used to overlooked and would consider the gaussian model alone.
Why do the sensor measurements have conditional independence (on the state), and not a general independence? In case of a lidar, each measurement is independent of the other irrespective of the state right?
Thank you, great lecture. Is it a part of an old series of lecture? Or just started an new series. As now a new semester is going to start. If possible can you just briefly describe a road map for this series. Most of you reading recommendations are from Probabilistic Robotics, can you please suggest more reading materials from other sources on SLAM, Sensor Fusion, Kalman Filtering and Particle Filters for Robot localization. Your lectures are of great help for researchers in the field of robotics.
@@CyrillStachniss Hi professor Stanchiss, do you have any recommendations for related hands-on project/exercises to enhance our understanding on these great concepts ?? thanks a lot
Thanks a lot Prof. Cyrill! Love the crisp handling of these topics.
Thank you very much for the explanation! This intricacy of sensor models was something I used to overlooked and would consider the gaussian model alone.
Thank you
Thanks, cyrill
Thank you very much for your valuable explanation! Expecting more lessons!
Why do the sensor measurements have conditional independence (on the state), and not a general independence? In case of a lidar, each measurement is independent of the other irrespective of the state right?
Thank you, great lecture. Is it a part of an old series of lecture? Or just started an new series. As now a new semester is going to start. If possible can you just briefly describe a road map for this series. Most of you reading recommendations are from Probabilistic Robotics, can you please suggest more reading materials from other sources on SLAM, Sensor Fusion, Kalman Filtering and Particle Filters for Robot localization.
Your lectures are of great help for researchers in the field of robotics.
This will be the recording of the next terms Mobile Sensing and Robotics 1 course
Does anyone know if the slides are available somewhere? I search Cyrill's website but didn't find them. Many thanks
Not yet (only the version from last year) but I will make a bundle as soon as all lecture have been recorded. Send me a personal email.
@@CyrillStachniss Hi professor Stanchiss, do you have any recommendations for related hands-on project/exercises to enhance our understanding on these great concepts ?? thanks a lot
@@riochuong105 Did you check the Probabilistic Robotics book chapters? I quite like them.