Grid-based mapping and tracking in dynamic environments using a uniform evidential environment representation

Tanzmeister, Georg; Thomas, Julian; Wollherr, Dirk; Buss, Martin

doi:10.1109/icra.2014.6907756

Cited by 75 publications

(71 citation statements)

References 16 publications

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“…The update step will also be carried out with the Dempster-Shafer theory of evidence, but this will be covered in the next section. In [10], the authors also proposed to use DempsterShafer theory and BBAs to represent the occupancy state of grid cells. In contrast to this work, they defined stationary and dynamic occupancies as different events in the frame of discernment.…”

Section: A Representing Dempster-shafer Occupancy Mass With Particlesmentioning

confidence: 99%

“…In the basic approach, we estimated the transition probabilities (9) and from there the predicted occupancy probability (10). The calculus does not change in the implementation approach, but now we interpret the result as transition mass…”

Section: A Representing Dempster-shafer Occupancy Mass With Particlesmentioning

confidence: 99%

“…Danescu et al presented results of the SMC-BOF based on measurements of a stereo camera. Two other implementations of the SMC-BOF use data of a multilayer laser scanner [9], [10]. These two publications vary slightly regarding the interpretation of particles but both independently report that grid cells representing elongated, stationary obstacles are often mistaken as moving with the same speed as the ego vehicle.…”

Section: Introductionmentioning

confidence: 98%

“…This paper adopts the SMC-BOF as suggested in [8]- [10] and proposes some adaptations. Additionally, the work presents details that are important for the implementation of the SMC-BOF.…”

Section: Introductionmentioning

confidence: 99%

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Fusion of laser and radar sensor data with a sequential Monte Carlo Bayesian occupancy filter

Nuß

Yuan

Krehl

et al. 2015

2015 IEEE Intelligent Vehicles Symposium (IV)

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Occupancy grid mapping is a well-known environment perception approach. A grid map divides the environment into cells and estimates the occupancy probability of each cell based on sensor measurements. An important extension is the Bayesian occupancy filter (BOF), which additionally estimates the dynamic state of grid cells and allows modeling changing environments. In recent years, the BOF attracted more and more attention, especially sequential Monte Carlo implementations (SMC-BOF), requiring less computational costs. An advantage compared to classical object tracking approaches is the objectfree representation of arbitrarily shaped obstacles and freespace areas. Unfortunately, publications about BOF based on laser measurements report that grid cells representing big, contiguous, stationary obstacles are often mistaken as moving with the velocity of the ego vehicle (ghost movements). This paper presents a method to fuse laser and radar measurement data with the SMC-BOF. It shows that the doppler information of radar measurements significantly improves the dynamic estimation of the grid map, reduces ghost movements, and in general leads to a faster convergence of the dynamic estimation.

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Section: A Representing Dempster-shafer Occupancy Mass With Particlesmentioning

confidence: 99%

Section: A Representing Dempster-shafer Occupancy Mass With Particlesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

“…This paper adopts the SMC-BOF as suggested in [8]- [10] and proposes some adaptations. Additionally, the work presents details that are important for the implementation of the SMC-BOF.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fusion of laser and radar sensor data with a sequential Monte Carlo Bayesian occupancy filter

Nuß

Yuan

Krehl

et al. 2015

2015 IEEE Intelligent Vehicles Symposium (IV)

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“…A computationally less demanding modification [54] produces a two-dimensional occupancy grid representation with a distribution of possible velocities for each cell. Another approach is described in [62,63] (and similarly in [244]), in which a particle filter-based tracking is used to infer the velocity of the grid cells. The raw occupancy grid serves as the measurement vector and the particles additionally provide the building blocks of the modeled world.…”

mentioning

confidence: 99%

Bayesian environment representation, prediction, and criticality assessment for driver assistance systems

Schreier

2017

At - Automatisierungstechnik

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The dissertation approaches the questions i) how to represent the driving environment in an environment model, ii) how to obtain such a representation, and iii) how to predict the traffic scene for criticality assessment. Bayesian inference provides the common framework of all designed methods. First, Parametric Free Space (PFS) maps are introduced, which compactly represent the vehicle environment in form of relevant, drivable free space. They are obtained by a novel method for grid mapping and tracking in dynamic environments. In addition, a maneuver-based, long-term trajectory prediction and criticality assessment system is introduced and the application of all methods within the advanced driver assistance system PRORETA 3 is described.

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FH-Net: A Fast Hierarchical Network for Scene Flow Estimation on Real-World Point Clouds

Ding¹,

Dong²,

Xu³

et al. 2022

Lecture Notes in Computer Science

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Grid-based mapping and tracking in dynamic environments using a uniform evidential environment representation

Cited by 75 publications

References 16 publications

Fusion of laser and radar sensor data with a sequential Monte Carlo Bayesian occupancy filter

Fusion of laser and radar sensor data with a sequential Monte Carlo Bayesian occupancy filter

Bayesian environment representation, prediction, and criticality assessment for driver assistance systems

FH-Net: A Fast Hierarchical Network for Scene Flow Estimation on Real-World Point Clouds

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