LIDAR-based people detection and tracking for @home Competitions

Álvarez-Aparicio, Claudia; Guerrero‐Higueras, Ángel Manuel; Rodríguez-Lera, Francisco J.; Olivera, Maria Carmen Calvo; Olivera, Vicente Matellán; Clavero, Jonatan Ginés; Rico, Francisco Martín

doi:10.1109/icarsc.2019.8733624

Cited by 7 publications

(6 citation statements)

References 16 publications

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“…On the other hand, considering the usage of a Kalman filter, Table 1 shows the average mean error and standard deviation for scenarios 1, 2, and 3. The results in scenarios 1 and 2 are consistent with the results shown in [11] and also in [23] keeping e PeTra in an expected value. e PeTra value are even slightly better at scenario 2.…”

Section: Discussionsupporting

confidence: 88%

“…e PeTra value are even slightly better at scenario 2. Regarding scenario 3, the reduction of e PeTra is considerably high compared to the results obtained in [23]. Figure 8 also allows us to visualize the evolution in time of e PeTra at scenarios 1, 2, and 3.…”

Section: Discussionmentioning

confidence: 76%

“…A first approximation for this method was presented in [23]. The system presented in [23] correlates two locations estimates considering its distance.…”

Section: Location Estimates' Correlationmentioning

confidence: 99%

“…A first approximation for this method was presented in [23]. The system presented in [23] correlates two locations estimates considering its distance. Each estimate at a specific time (t) is matched with the nearest estimate at time t + t. The nearest estimate is chosen from the Euclidean distance.…”

Section: Location Estimates' Correlationmentioning

confidence: 99%

“…This paper presents a solution to the problem of the correlation method described at [23] when people cross their trajectories. Instead of Euclidean distance, a Kalman filter is used to predict the direction of each person identified by PeTra.…”

Section: Location Estimates' Correlationmentioning

confidence: 99%

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People Detection and Tracking Using LIDAR Sensors

et al. 2019

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The tracking of people is an indispensable capacity in almost any robotic application. A relevant case is the @home robotic competitions, where the service robots have to demonstrate that they possess certain skills that allow them to interact with the environment and the people who occupy it; for example, receiving the people who knock at the door and attending them as appropriate. Many of these skills are based on the ability to detect and track a person. It is a challenging problem, particularly when implemented using low-definition sensors, such as Laser Imaging Detection and Ranging (LIDAR) sensors, in environments where there are several people interacting. This work describes a solution based on a single LIDAR sensor to maintain a continuous identification of a person in time and space. The system described is based on the People Tracker package, aka PeTra, which uses a convolutional neural network to identify person legs in complex environments. A new feature has been included within the system to correlate over time the people location estimates by using a Kalman filter. To validate the solution, a set of experiments have been carried out in a test environment certified by the European Robotic League.

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Section: Discussionsupporting

confidence: 88%

Section: Discussionmentioning

confidence: 76%

“…A first approximation for this method was presented in [23]. The system presented in [23] correlates two locations estimates considering its distance.…”

Section: Location Estimates' Correlationmentioning

confidence: 99%

Section: Location Estimates' Correlationmentioning

confidence: 99%

Section: Location Estimates' Correlationmentioning

confidence: 99%

See 3 more Smart Citations